{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年5月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../py')\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and career='机器学习'  and monthly_salary<80000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>zhinengleibie</th>\n",
       "      <th>career</th>\n",
       "      <th>real_province</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>company_id</th>\n",
       "      <th>...</th>\n",
       "      <th>bd_hbase</th>\n",
       "      <th>bd_storm</th>\n",
       "      <th>bd_pig</th>\n",
       "      <th>bd_mahout</th>\n",
       "      <th>bd_impala</th>\n",
       "      <th>bd_yarn</th>\n",
       "      <th>bd_alluxio</th>\n",
       "      <th>bd_flink</th>\n",
       "      <th>bd_presto</th>\n",
       "      <th>bd_heron</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>110043859</td>\n",
       "      <td>12500</td>\n",
       "      <td>31</td>\n",
       "      <td>机器学习开发工程师</td>\n",
       "      <td>高级软件工程师</td>\n",
       "      <td>机器学习</td>\n",
       "      <td>None</td>\n",
       "      <td>四川</td>\n",
       "      <td>成都</td>\n",
       "      <td>co4977334</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_id  monthly_salary  headcount      title zhinengleibie career  \\\n",
       "16  110043859           12500         31  机器学习开发工程师       高级软件工程师   机器学习   \n",
       "\n",
       "   real_province province city company_id  ... bd_hbase bd_storm  bd_pig  \\\n",
       "16          None       四川   成都  co4977334  ...    False    False   False   \n",
       "\n",
       "   bd_mahout  bd_impala  bd_yarn bd_alluxio bd_flink bd_presto bd_heron  \n",
       "16     False      False    False      False    False     False    False  \n",
       "\n",
       "[1 rows x 114 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305, 114)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "876"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7000., 12500., 18500., 27500., 45600.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年5月中国机器学习工程师的平均工资为21826元，工资中位数为18500元，其中95%的人的工资位于7000到45600元之间。\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月中国机器学习工程师的平均工资为{salary_mean:.0f}元，工资中位数为{q[2]:.0f}元，其中95%的人的工资位于{q[0]:.0f}到{q[4]:.0f}元之间。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2020, Developers in China earn 21826 Yuan as average, the median is 18500 Yuan, 95% of them earn between 7000 and 45600 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print(f'In {year}, Developers in China earn {salary_mean:.0f} Yuan as average, '\n",
    "      f'the median is {q[2]:.0f} Yuan, 95% of them earn between {q[0]:.0f} and {q[4]:.0f} Yuan.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Functions\n",
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "    \n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >career</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031efrow0_col0\" class=\"data row0 col0\" >机器学习</td>\n",
       "                        <td id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031efrow0_col1\" class=\"data row0 col1\" >21826</td>\n",
       "                        <td id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031efrow0_col3\" class=\"data row0 col3\" >18500</td>\n",
       "                        <td id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031efrow0_col4\" class=\"data row0 col4\" >45600.000000</td>\n",
       "                        <td id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031efrow0_col5\" class=\"data row0 col5\" >876</td>\n",
       "                        <td id=\"T_f4e26e5c_8b6a_11ea_b693_701ce71031efrow0_col6\" class=\"data row0 col6\" >100.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25eb4f612c8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21825.99086757991"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "876"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    305.000000\n",
       "mean       2.872131\n",
       "std        2.639536\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        2.000000\n",
       "75%        5.000000\n",
       "max       31.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "876"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >city</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow0_col0\" class=\"data row0 col0\" >北京</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow0_col1\" class=\"data row0 col1\" >29107</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow0_col2\" class=\"data row0 col2\" >8708</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow0_col4\" class=\"data row0 col4\" >58875</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow0_col5\" class=\"data row0 col5\" >141</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow0_col6\" class=\"data row0 col6\" >16.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow1_col0\" class=\"data row1 col0\" >深圳</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow1_col1\" class=\"data row1 col1\" >27064</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow1_col2\" class=\"data row1 col2\" >7720</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow1_col3\" class=\"data row1 col3\" >26250</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow1_col4\" class=\"data row1 col4\" >52440</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow1_col5\" class=\"data row1 col5\" >152</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow1_col6\" class=\"data row1 col6\" >17.35%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow2_col0\" class=\"data row2 col0\" >上海</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow2_col1\" class=\"data row2 col1\" >25045</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow2_col3\" class=\"data row2 col3\" >22583</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow2_col4\" class=\"data row2 col4\" >56375</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow2_col5\" class=\"data row2 col5\" >170</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow2_col6\" class=\"data row2 col6\" >19.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow3_col0\" class=\"data row3 col0\" >广州</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow3_col1\" class=\"data row3 col1\" >18694</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow3_col2\" class=\"data row3 col2\" >6479</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow3_col3\" class=\"data row3 col3\" >17500</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow3_col4\" class=\"data row3 col4\" >35000</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow3_col5\" class=\"data row3 col5\" >90</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow3_col6\" class=\"data row3 col6\" >10.27%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow4_col0\" class=\"data row4 col0\" >杭州</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow4_col1\" class=\"data row4 col1\" >18235</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow4_col2\" class=\"data row4 col2\" >7875</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow4_col3\" class=\"data row4 col3\" >19000</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow4_col5\" class=\"data row4 col5\" >68</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow4_col6\" class=\"data row4 col6\" >7.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow5_col0\" class=\"data row5 col0\" >南京</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow5_col1\" class=\"data row5 col1\" >14887</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow5_col2\" class=\"data row5 col2\" >7606</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow5_col3\" class=\"data row5 col3\" >12750</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow5_col4\" class=\"data row5 col4\" >36075</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow5_col5\" class=\"data row5 col5\" >71</td>\n",
       "                        <td id=\"T_029dc18a_8b6b_11ea_acd4_701ce71031efrow5_col6\" class=\"data row5 col6\" >8.11%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25eb5907cc8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_col(data,'city')\n",
    "data_pl=data_pl[data_pl['head_count']>50]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21004566210045683"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].sum()\n",
    "other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['city'].values)\n",
    "labels.append('其他')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.1f%%')\n",
    "plt.title(f'算法工程师去哪里')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow0_col0\" class=\"data row0 col0\" >3</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow0_col1\" class=\"data row0 col1\" >matlab</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow0_col2\" class=\"data row0 col2\" >25410</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow0_col3\" class=\"data row0 col3\" >24167</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow0_col4\" class=\"data row0 col4\" >7000</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow0_col5\" class=\"data row0 col5\" >62500</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow0_col6\" class=\"data row0 col6\" >95</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow0_col7\" class=\"data row0 col7\" >6.88%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow1_col0\" class=\"data row1 col0\" >4</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow1_col1\" class=\"data row1 col1\" >r</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow1_col2\" class=\"data row1 col2\" >24639</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow1_col4\" class=\"data row1 col4\" >11917</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow1_col5\" class=\"data row1 col5\" >37500</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow1_col6\" class=\"data row1 col6\" >66</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow1_col7\" class=\"data row1 col7\" >4.78%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow2_col0\" class=\"data row2 col0\" >5</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow2_col1\" class=\"data row2 col1\" >cpp</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow2_col2\" class=\"data row2 col2\" >23198</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow2_col3\" class=\"data row2 col3\" >20000</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow2_col4\" class=\"data row2 col4\" >7000</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow2_col5\" class=\"data row2 col5\" >54150</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow2_col6\" class=\"data row2 col6\" >377</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow2_col7\" class=\"data row2 col7\" >27.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow3_col0\" class=\"data row3 col0\" >6</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow3_col1\" class=\"data row3 col1\" >python</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow3_col2\" class=\"data row3 col2\" >22511</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow3_col3\" class=\"data row3 col3\" >20000</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow3_col4\" class=\"data row3 col4\" >8000</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow3_col5\" class=\"data row3 col5\" >48960</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow3_col6\" class=\"data row3 col6\" >508</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow3_col7\" class=\"data row3 col7\" >36.81%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow4_col0\" class=\"data row4 col0\" >9</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow4_col1\" class=\"data row4 col1\" >java</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow4_col2\" class=\"data row4 col2\" >20478</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow4_col4\" class=\"data row4 col4\" >9000</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow4_col5\" class=\"data row4 col5\" >40000</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow4_col6\" class=\"data row4 col6\" >213</td>\n",
       "                        <td id=\"T_0e0a6d76_8b6b_11ea_98c4_701ce71031efrow4_col7\" class=\"data row4 col7\" >15.43%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25eb57642c8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "data_pl=data_pl[data_pl['head_count']>50]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xkWI6gIi/UF6gP/AOcG+vCk688L3I+6YzCZGgvgasDOYHzzQdRMSHrBQ9JpQXuAD7UovOhx7TUHXLdb59pV3VAHPJhEhoGngYuL1oZlGj6TAidqQUPSKUF0gFHgVuaO7z1ekUXXOLb6SVonzxTSZEUvkYuKRoZtFG00FEbMjuUw8I5QV6Ae9yhEIE6FBP8AevWe/FL5UQSWkC8EkwP3i+6SAiNmSl6HKhvMAJwDyg79GeqyF812W+dWsGqpGxTyZEUtPAfcAvi2YWWabDiOiRlaKLhfICVwMLaEEhAijw/7wgkpnWqGtim0yIpKeAnwOvBPODuabDiOiRlaILOccPHwG+35bXr+vDwl/M9E+NbiohXGstcF7RzKJi00FE+8lK0WVCeYEewP9oYyECDN/J1GlF1kfRSyWEq40AlgTzg3JrNReQUnSRUF5gArAMmNLebd3wujUot1rvbX8qITyhI/au1J+bDiLaR3afuoRz/eFzQEa0trmnI0tvvNF/fLS2J4RHPAncIEPFk5OsFF0glBe4HiggioUI0P0Ax1+yICKXaQjROt8F/uXchk0kGSnFJBfKC/wSeIIY/b/81gf6uL579dZYbFsIFzsHmB/MD3Y3HUS0juw+TVKhvIAC/oB9u6eYqklj9dW3+kZYKUpuOixE62wEziyaWbTBdBDRMrJSTELOJRd/Jw6FCJDVwKibX7FkaLgQrTcU+CCYH5Rj80lCSjHJhPICHYBC4JJ4vu+kYn1ycLO1Kp7vKYRLdAfeCeYHp5sOIo5OSjGJhPICXbFv+fT1eL+3Av/tL1k56Q26Ot7vLYQLdAD+HcwPfs10EPHVpBSTRCgv0A94DzC2G8ZvMXDO85Hlpt5fiCSXCbwazA+eZTqIODIpxSQQygv0xr7LRcB0lqG7mHLqCmuJ6RxCJKkM7Ms1zjUdRDRPSjHBhfIC3bHHtg0zneWQ696whnaq0ntM5xAiSaUBLwfzg982HUR8mZRiAgvlBToD/yUBVohNpUC3+5+JbDGdQ4gklgoUBPODF5sOIr5ISjFBhfICOcB/gLGmszSnaxUTL38nstB0DiGSmB/4m+xKTSxSigkolBfIAl7H4Ek1LfGNJXpC/916s+kcQiQxP/aKcbrpIMImpZhgQnmBDOBVonCni1hTkHXvc5FaX0Q3ms4iRBLLwD4rdbzpIEJKMaE4k2peBk4znaWlMhsYees/rQ9M5xAiyeUA/wnmBxPq/AEvklJMEKG8gA94AZhhOktrTVyvTx69ySoynUOIJNcN+G8wPzjQdBAvk1JMHI8A55sO0RYKfLf9Pys3s14fNJ1FiCTXD3grmB/saTqIV0kpJoBQXuBG4jTcO1b8FgPmPB9ZaTqHEC5wDPYxxkzTQbxIStGwUF7gDOBR0zmiYXAZJ39tubXYdA4hXOB4ID+YH1Smg3iNlKJBobzAKOAlwGc6S7Rc86Y1vMsBXWY6hxAucCFwj+kQXiOlaIgzvu01oKPpLNGUAl3unxvZjty9Woho+HkwP3i56RBeIqVoQCgvkA78ExhsOkssdK5mwsy3rfdM5xDCJZ4K5gdPMh3CK6QUzXgKcPVf8rM/1hMHlumNpnMI4QLp2HfWGGI6iBdIKcZZKC/wC8D1u0MUZN7zXKRRpt0IERXdgNeC+cFs00HcTkoxjkJ5gW8Bd5vOES8ZjeT9eJ61yHQOIVxiJPZeJhFDUopxEsoLDAKeBjx1ivVxG/XUcRssuX7RoHBlGB2W855c4uJgfvBm0yHcTEoxDpyZpi8CnUxniTcFKT+dZ3XNrNMHTGdxi0hNhC0PbWHzbzez9Q9bscIWYJffhl9uAKD87XI23rURq97i4KqDKL+nfhZzN60ffOTRAQl9B51kJqUYH/cBJ5gOYYrPot89f4vIbNQoqfiwgm5ndGPwTwaTmptKVVEVAKUvlmI12AVZt62OzlM7U7OphpQ0+WfuFkrrinv27ltxy/7KeczJ7Wo6jxvJv5YYC+UFzgZmm85h2oA9nHTWR9aHpnO4QdfTupJ9rH2+RfhgGH+On6o1VaSkp+DP9QOgtUZHNFWrq8gZnWMyroiSLMtaU7ijtOq8qurjsWekPsucXNkFEGVSijEUygv0BfLx2HHEI7nqbSvQtVKXms7hFjUbaohUR8gYlMGeV/fQ68Jen30u59gcDq44SGrnVLY+upWqUJXBpKK9xtTVL3xv646hA8Lhfk0ePhv4ialMbiWlGCPOraD+jn0qtQAUdLp/bqRUpt20X7gqzM6/7aTvNX3Z+/peupzaBV+Hz6cF5p6QS4/zeuDL8pEzJocDH8sh3aSkdfWP9u1f9LfSsqlp9vWKh/s1c3JPjHsuF5NSjJ1fAlNNh0g0nWo47ur/WgtN50hmVthi++Pb6XVBL9K6pVG1uory/5Wz6f5N1G2ro+TpEgDqy+pJ65Fmn2QjP4YknTRLb3xp565dsyoPftWgDz/wAnNyXTUu0iQpxRgI5QVOAX5hOkeiOmO5PmFIqV5vOkey2r9wP7Vba9n92m423b+Jrqd1ZcjtQxhy+xAyBmTQ9+q+RGojpOamkt4nnf3z99NhZAfTsUUrDG5o/OC9bTt6BRoah7bg6QOAh2KdySuU7MmKrlBeoDOwGuhtOksiq/ezbtatvoFhv2pul5AQ3qR1w8wDBxf/eF9FW/YyfZ05lW9FPZPHyEox+h5GCvGo0sMM/9nLcu9FIQ7xab3jmV27N7SxEAGeYk6unGrcTlKKURTKC3wNuMp0jmQxerOeMnGt9YnpHEKY1jMc/mj+tpIOE+rqR7ZjMwOA30Urk1fJ7tMoCeUFOgBFuPR2ULESUey89mZfh+pMlWs6ixBxp3Xk3Krq9+/du2+qit6lW19jTuXbUdrWZ5RS84GPgNFa6zOivf1EISvF6LkHKcRW82n63PNcZLXpHELEm9J6z6O79xb9eu++aVEsRIjdbtRJwIduLkSQUoyKUF7gBECG9LZRv3JOPGeJ9YHpHELES24ksvKt7TutU2tqx8Zg8wOB38Zgu6u01v+IwXYTipRiOznDvp9C/izb5Yp3rFHdK/RO0zmEiCmt9ck1tQsWbCs5tmck0jOG73Qdc3JPi/I2PTEWSb6Rt9/twLGmQyQ7Bbn3z42UybQb4VpaV969d99HT5TtmeYD39Ff0C4Kezeq3JS4laQU2yGUFxgJ/Nx0DrfoWMu46/4j026E+2RZVqhwR+mBb9nDvONlEDAnWhvTWk+P1rYSmZRiG4XyAvZPYpBmOoubnLZCTx66U68znUOIaAnaw7yHDAyH+xt4+x8yJ3eEgfdNWlKKbXcFMNl0CLdRkDbn+Yjyh3W96SxCtIvWNbfsq1j09yMP846HVOBRQ++dlOQ6xTYI5QUygXXY9zQTMbBqoFpw92W+aaZzCNEWaVpvem7nLmtkQ+Mw01kc32RO5aumQyQDWSm2zY+QQoypUVv11Ekha7npHEK01qCGxg8Wbt3RI4EKEeD3zMmVOcMtIKXYSqG8QE/gNtM53E6BuvkVq3d2ra4wnUWIFtG64crKAwtfKyk9sYPWiXbW5xDgFtMhkoGUYuvdBSTaX3hX8ml635sfCZnOIcTR+LTe8dddu9f/pO3DvOPhdubkyk3Pj0JKsRVCeYFRwLWmc3hJn/1MPu8Da5HpHEIcSQ9nmPfxdfWjTGc5ilyieImGW0kpts5vif1Ft+Iwly6wgj336x2mcwjxBVpHZlRVL3h7+84JnSyrs+k4LfQ9uUTjq0kptlAoL3A6cJbpHF6koON9cyPlSmvLdBYhwB7m/fvdez99YE95tId5x5ofeNB0iEQmpdgCobxACvCQ6RxellPHmBtet94znUOIjpHIyv9u3xk5vaZ2nOksbXQuc3LlGusjkFJsmSuB0aZDeN20In3i8B262HQO4V0n1dTOX7CtZFSvSKSX6Szt9AvTARKVXLx/FKG8gB9Yi31KszCswcfGq2/19WlIVZmmswgP0bryV3v3FV9QVX2C6ShRNJ45lXIt8GFkpXh0lyGFmDDSIgy9oyCy1HQO4R2ZllX82o7SSpcVIshqsVlSil/BOZZ4h+kc4osC25l60mrrY9M5hPsdW1//3nvbdgwaFA4PMJ0lBs5jTq7c9u4wUopf7SJATl9OMArUTa9Z/XNq9D7TWYRLaV1z076K91/YWTYlXZNhOk6MKOTWd18ixxSPwLk11KfIDYQTVlknFt90g3+S6RzCXVK13vzczrLwqIaGY0xniQMLCDCnUm7X5pCV4pGdgxRiQutZwaQL3rfeN51DuMfAxsYPF27d0c0jhQh2B8ghoiakFI/sp6YDiKO78D1rTK99ervpHCLJad3wncoDCwp3lE7O1jrHdJw4+w5zcgebDpEopBSbEcoLTAZONp1DHJ2CnPvmRvanWDpiOotITj6tS57atXv9bfsqvHr/Tj9y55/PSCk272emA4iWy65n9I2FMu1GtF73cPjjd7eVZJ6Q+MO8Y+0q5uT2MR0iEUgpHiaUFxgBnGs6h2idk1frkwLb9BrTOUSS0No6q6p6wf+27xzf2bK6mI6TANKA602HSARSil92A8k14FcAClJ/8WIkM61R15jOIhKb0nrvQ7v3rngw+YZ5x9p3mZObajqEaVKKTYTyAhnAFaZziLZJjTD4zhciclG/OKKciPXpm9t3Nn69pvY401kSUC/gfNMhTJNS/KILANmVksRGlDB1apH1kekcIvFMqq1dsHDbjpG9I5HeprMksBtNBzBNSvGLrjMdQLTf91+3BuVW672mc4gEoXXlnXv3LX5y155pfvtMS3FkJzMn19N3BJJSdITyAnnAFNM5RPulaLrfNzey0XQOYV6GZa19taS04qKDVTL56Ctojd6jc5fd2Thr8fC6/GtN5zFJxrw5QnmBh4FbTecQ0TPvRPVewTSf/KDjUSPr699/trRsgotnl7abpVX5Qiu46u7wlYM26T4DnYcPAL23PDDDkyetya4EIJQXSAdmms4houvbH+jj3h+lt5Z0UwOP/mzhGlrX3lhRuez6igMygOMI9usOnz4R/mbV3MgZ4xtIPXxoQUfgYuAZA9GMk1K0yQk2LqSgw73PRjZfc4svYqUon+k8IvZStd6Sv7OsIdjQIIV4GK2pXKIDK+5uvKLfGj3oaMcNr8OjpSi7T4FQXmABMNV0DhEbH+SpBY98y+fVEV6e0b+x8cOCkl2jcrTuaDpLIjmoM1c/GT57/18i54yvIz2zFS8dvuWBGetjFixBeX6l6EywkUJ0scnF+qS3t1irVw1K8fooL3fSuvHSg1Uf3FG+X37wcWhN1Qo9dPk9jVf0WK6Ht/Xv/WXAXdHMlQw8X4rIsUTXU+C/o8DKnnWrqq5PUx1M5xHR49N655927Sk/sa5OChGo0Wlr8yNn7H4sfN7YajLb+8P+pXiwFD2/+zSUF1gHeOXeaZ62oTfv3XGVX85GdYlu4ciyeSWlg7pYVlfTWUzSmtqQHrD81+HLOy2yjo323pDxWx6YsTzK20xonl4phvICQaQQPWNYKVOmr7SWzh+TcrzpLKIdtLbOqK5578E95VNSPHytdb1O3fhi5JQdD4cvGFNJ9kkxepvLAE+VoqdXiqG8wF3AL03nEPFjwd7rb/LpimzV3XQW0XpK6/IH95RvObO6Zny0tllWZdElU5HqS/zZ4FrTsFH3/vj+8GUd/meNHxOHt9wBDNjywAzPFIVnf8pyeH74rdekQLf750a2mM4hWi8nYhX9Z8fOhvYUYlmVxbj/q+KxpQ1MfLKK6gbNmxvDCV+Ijdq37aXwtAUT6p84cHrDQyfGqRAB+uGxExE9u/vUGesmZyN6UNeDTPzOu5H3nj9Fpt0kixNq6xY8sWv3ianQrlsb/fitemobYcWuCNeOS+OjnRE6pCZmIWpNeLvuvuzB8CX+QmvScaAGGIpyKbDA0HvHnZdXirJK9LBzF+vx/fbozaZziKPQ+sAv9u5b/NSu3dPaW4jvbA7TIRV6ZSu0hkYL/rsxzFnHJNbaIKxTdr4ambzgxPo/7p3a8OgJhdbk8aBMNvc3B932emL+5BADXi7FC0wHEOYoyPr1s5HaFEuHTWcRzcuwrLX/Kindd3EUhnk3RDT3LKzngdPtMahfH+qncF0j/ScYR9cAACAASURBVDqmcO4LNby72exfA62xdunOH/+08btLh9c/2/OHjTdNK6VrL6OhPtcLmGA6RLwk1o9IcRLKCwwBxprOIczKbGDkrf+0Fjx0vky7STR59Q3vP1daNj5D69ZMYDmiB95v4PsT0uiUYS94Lj42lYGdFJv2a2Yc42deqJFTBsf/22FEqz3vWMetuSd8+dBtumciF885gCfuU+rJUkR2nQrH8ev0ycHNVlHR4JSg6SwC0Lr2hooDy75fURnV2aVvbwrzzmZ4/KMG+3jiq7VMG+hjeNcUKuoUVhzPrdQaXU7HFX8Mf6v++chp48P4k+GHsnOAX5kOEQ+evCQjlBdYDJxgOodIDOEUtl19q69LXZrKNp3Fy/xab80vLasbXd8wIpbvM31uNa9emsWSHREm9fMx5ZlqfjktnW8H2nXI8qgsrfYtskYV3RO+YsA63X9wTN8sNvpteWBGiekQsea5UgzlBboAewHPHDgWR7epJ+/ddrVMuzGlX2N48UslpSPdOMy7UncoeiL8jYNPR84a30Bquuk87fC9LQ/M+IvpELHmxd2n05FCFIcZUsaU05dbi98+LkXu0B5PWjdefLDqg1+4bJi31hz4WI9YcXfjFb2L9BC37Jo/B3B9KXpxpfhH4Aemc4jEY8G+79/oa9zXUfU0ncULUrQu/VPZnr0n1da5pTSo0hmhpyNnlv85fO64GjLcNny+Bui65YEZdaaDxJIXV4qnmg4gElMKdLkvP/Lx9Tf5pRRjrKs9zHtgV8tK+kLUmuoiPXj5PY1XdPtI5wVM54mhLGAy8K7pILHkqesUQ3mBnsBI0zlE4upSxYQr344sNJ3DtbS2vlZds+Cd7SXjulpWN9Nx2qNWp61/Mnz2wmD9U5FzG349xeWFeIirdnM3x2srxVNMBxCJb8ZHeuKCoN64tacaajqLmyityx/YU7757OqapP3GqjV163S/Zb8Of6fjQmuMF++y4/o5qJ46phjKC/wfcJ3pHCLx1aUSmnWrb1jEp2J7nr5HZEesonk7S7v1CUd6m87SFg3av/mlyLRtvwtfNLqCnM6m8xhUC3Ta8sCMBtNBYsVrK0U5nihaJKORwOx/WPMfvNA33XSWZDextm7h/+3aPbm9s0vjTWsaN+teH/8mfGnmm9bEsUAyXlsYbZnA8cD7poPEimeOKYbyAv2AYaZziOQxfoOeOnaj9anpHElL64O379334dO7dk9NpkJs1L4d8yJTFpxQ/3jFqQ0PT3YKUXwu7ru/lVLTlVKDmnl8/lFeN7e5130VL60UZZUoWkVByk9ftrpcc7M6UJuhXHdReSylW9b6F3eW+Yc1Nk42naUltCZSQrdlv2u8KOUV68TjNCn9TGdKYNOAX8f5PacD84EtsX4jzxxTDOUFngZmmc4hks/W7iz6ybX+k0znSBbD6xvefz6Kw7xjKaJTSv9jTVx7X+NlI0ronpTHOw2oxj6u2O5biyillgG7gQbsu3G8ApwNaGCh1vrnSqlnsE+SrABWa62/0+T187XW053f9wFeOuy1c7F3+fYDlmqtbz1aJs/sPsVDtz4R0TVwDyed+bH1oekcCU/ruuv2V74/b+eukxO5ELVGl+lOy25vvGbpMfXP9rix8ebpUoit0gEYHaVtZQEXOtu7DPgacBtwFvANAK31LGAucEvTQmxG38Nf63hda30SMFIpddRd4Z7YfRrKC6QDXriGSMTIrLesvI+Gq13lHVWi3OMuofi13jq3tKxuTH1DVO9uEU2WVnvmW2PW3BO+fPBm3We86TxJbgKwPArbKdNaVymltgIR4AB2sVUBOa3cVhj7Th6Hv3aJ89/lwFBgxVdtxCsrxWPxyA8AIjYUdL5/bqQErxxvaIW+jeHFC7bt6DQmxne3aKt9OmfF3Y1XfDCiPj/36safTtus+wwwnckFYrXn7QBwP3At9m7QQ2qxV5UopY40u/pHR3jtoR+ARtOCY5JeKQo5e0y0W6dqxs96y1rwzNflpsQAaB2+8GDVol8m4DBvS1Ox2Bq58u7wlf2L9QD59x99sSrFC7C/X+8BapRSfbXWJcA84Gml1C+By4ENzby2EPhz09ce2qZS6kbgE631sqMF8MSJNjIEXESLhrrbZvl2bO6lPH15T4rWux4r27N7Sm1dtI4tRcUBnbnqL+FzKp+MzBhfT1qG6Twu1ghku/EiflkpCtEKCjLufi4SmfUjX0PYp9JM5zGhSySyfF5Jaf9uESshClFrDi7Xx3xyd+MVvVbqYceazuMRqdhzpL/y+Fwycv0xxVBeQAFjTOcQ7pEeZsRPXvbg2aha69Oqaxa8u61kbLeI1d10nBqdXvx4+JvvHVv/V3V+w11TV+phw01n8hhXLja8sFIcSuvPYhLiK43dpKdMWGet+Hh4iiu/MRxOab3v/j3lm2YYHuatNTVr9MDl94Qv77LYGjUSyDOZx+NcudjwQil64puWiC8FKbP/YXW/5hZVWZOhck3niaUOlrX65ZLSTv3CEWPX+tbp1A1/j5xW8kj422MPkJ2wl314TNLfC7M5UopCtJFP0/feZyMf/Og6/4mms8TK+Nq6hX/ZtXtymoHZpVpTv0H3XXZf+LLsd61xo5HZxYnGlbfNcv3Zp6G8wGvAOaZzCPfKPy3lg9ePT3FXMWp98Gf7KlZdfuBg3GeXNmjf1n9Epm55MHzRsfvI7Rrv9xctpoGsLQ/MqDMdJJq8sFKUG8WKmLryf9aopcPVzj2dVB/TWaIh3bLW/31nmX94HId5a014m+7x8W/Cl6b92zp+HKiB8Xpv0WYK+/vratNBoskLpSj/uERMKci9Lz+y6bs/9PXmyNM2ksKwhoZFf99ZNi5T66x4vF9Yp5QUWpM2PNB4Wd4uukyKx3uKqBqOlGLyCOUFuuOMBhIilnJrGPfd/1gLnjwrSafdaF1/beWBpTfvr5wS+7fC2kWXZQ+FL2ReZMp4TUrfo78qcUSq95OSkYPyufrbZ0u57rii2/+vyipRxM3pK/Skd8bodRv7qKS6Xs6v9bZnSstqxtY3xLQQI1qVvWWNL/51+PJh23WPibF8r2gp/++fyBwynnDlbqpXvUPPS++ndvNyso89zXS0ROG6UnT7xfuDTAcQ3qEgfc7zEeUP63rTWVqqd2N4yfxtO3LH1jfE5Ho/rdF7dO6yOxtnLR5e/2zX6xt/NG277pEUK8O67auIVO8na9gJNO7eTPaYr9Owax0qVabHNSGlmGRkpSjiKj3MMbe/ZC02neOotA5/+2DVgv/u2HlCrqWjfp2lpVX5/MjoBac1/G7bxPonxj8X+dqkCMmzv1FHwpT/5zH8HXtQs34xWmt0JEzt5k/IHCJ3nWrCdZfJSCkKEWXHbtVTTyi2onGvuZhI0brs8bI9q+/auy/qxz/36w6f3td42Qd59XOzr2q8bdom3Scp/w1WrXqH1G79yT3hAupL1+Hv2J3ajR/hz+nGnnn3ULf1U9MRE0WvQbe97qoecdUX04xBpgMI71GgbvmX1Tu7VleYznK4zpHIJ29vL0mZWlsXtRFdWlO52AosOLv+vo3j6p8c/ZfIOSc2kJoere2b0Lh7IzljzsSX3ZnskafQsGcznU66jJSMDmQOnUjNukWmIyYKH2B8Dm40ub0Uk/KnVJH8fJre9z4bWWM6x2e01tPtYd6ju0dpmPdBnbn64cbz3w/UP5N2ScOd09boQa65JtjfqQ/hil0A1O9aj79jDxr378TfqTfKlyr3mv6iXqYDRFPS7ONvIylFYUyffZz4zQ+tD16ZbHbajdJ6/717yzecW9X+Yd5aU7VSD/3k7sYrui/Xw0dFI18iyh79NcrfeJTq0EK0Fab7uT+jcV8JqV37U/7mY3Q68VLTERNJb2Cl6RDR4toxb6G8QC6QcLuvhLdoqLzpBl/V7k7KyBmXHSxr9f8r2ZXbPxzu157t1Oi0tc9Fvr77D+Fvja0mU+46I5q6essDM54xHSJa3LxS7GY6gBAKcu+fG9l87c2+3lqpuB6uGFdXt/Cp0t2T0qBNN0PWmtqQHrD81+HLOy2yjh0FjIhyROEOsvs0SXQ0HUAIgJxaxl7/b2vBEzPiNO1G66qf7Kv49MoDB6e25eX12r/pxcgpOx4OXzi6kuyToh1PuE5v0wGiyc2l6Op73InkMv1TPfntsXrt+r4qpqutNEtv/PvOXWpEY2OrjmNqTcMm3fvj+8OXdnjbmjAGGBKjiMJ9pBSThJSiSBgK0n71fMQ/60e+uka/islIlKH2MO+xWVp3aOlrGrVv2z8jJ2/+TfiSUeXkuuv2VyJeOpkOEE1SikLESVqEoXe8GFlw1+X+6O5G1br+6sqDS2/dX9Gi2aVaE96huy97MHyx/zVr8nGgBkQ1j/CabNMBoklKUYg4GrmdqSeusZZ9MDIlKrPC/Fpvf6p0d9X4+vqjFmJYp5S+YR2/9v7Gy0bspNsJ0Xh/IQBXnY0spShEHClQN71q9f10kNpflaU6t2dbvcLhpS+XlI7ItXT/Iz1Ha6wyOi9/OHyB9XJk2niLFFcd/xEJQVaKSULOPhUJyafpdV9+ZPEPb/C37aa6WkfOq6p+/+69+6Yq++7nXxLRas+71rg194QvH7pV95rQrsBCfDVZKSYJWSmKhNWrgknnv28tmndySqsueUjReveju/funF5T+6Xjklqjy+m44o/h8+qej5w+IUyUj10K0TxXrRTdPNHmReBi0zmEOBINB2/+nq9iVxd1xN2fTXWKRFbMK9nVp0ck0qPp45Zm/yLr2E/vCV8xYJ3uPzg2aYX4SulbHpjRYDpENMhKUQhDFOTclx/ZfO3Nvj5WivId8Yla66m1dQv/ULbnZJ99VwIAKnVW0Z/D3zjwdOSs8fWkyapQmJQN7DMdIhrcXIqppgMIcTTZdYz+fqE1/7FzfdOb+7zSuuKevfvWf7OqehqA1hz4WI9YcXfjFb2L9JBgXMMKcWSZpgNEi5tL0Z37hYXrTFmtT3p7nA4V91eBpo9nWdaa/1eyq+OAcHhilc4IPR05s/zP4XPH1ZDRpvFtQsSQa25D6OZStEwHEKIlFKTe+UIkfdatvtqGVJUJMKaufuFTpWXHrrMGr57deMXBj3Re4GjbEcIgKcUkIKUokkZqhCG/eCGy8JdX+MZfvF8v7Fk+vsOE8Lf9VWS1aEqNEIZJKSYB2X0qksrwnSmTL50/trAqrHpsBnUur64ynUmIlihP66phhukYUeHmUpSVokgqWwaevbgDPXulN86fbDqLEK3RtXG/axYhrlnyNkNKUSQNS6U0bh3w9cG+9DETQJWZziNEK4VNB4gWKUWXqohE+KC6mv3hcLMfN7U3HKbRpUMcksWmwd9YrFN8/ZTypaakDguZziNEK0VMB4gWN5eiZ7/LV0YifH/HDj6tq+Wq7dvZFw5/6ePn9+/noq1bqLEsFlVXk6qaHaEp4sBS/vpt/U8beujj1KzpebjoJ2/hCa4pRTmm6ELr6uv5WY8ejMnM5EDE4oWKii98vKa+juL6Os7PzWVVXS2ZKVKIJm0ccu4SlO+zaw9VSk4vlZK7WFuVbRsYLkT8uaYU3bxS9GwpTszKYkxmJh/X1FBUV8vMzp2/8PHYjEw0ENawqLqGKR1cNc83qURS/HXb+51yzOGP+zOnpZvII0QbSSkmgXrTAUzSWvPGwQN09PnwK/Wlj0/K6sCCqip6+f3cWLKDJTXVpiN70sYh31qK+vI9Dn1pw8aBb6OJTEK0kgaqTIeIFjeXYqXpACYppbizZy9GpKfzblXVlz4+q2NHbuzWjRxfCtM6ZPPWwYOmI3tOJCW1dkffqXlH+rwvfUxJPPMI0UYVswsKZaWYBDxbik+Vl/NKpf3lH4hEKGls/MLHOT77f/vWhgb6p6aRppR39zUbtH7o+UtRKT2O9Hl/5uRxuOgncOFa5aYDRJObS7HCdABTLuzUiVcPVHLFtq1YzXx8UlYHqiIRuvn9DEtP46XKCiZndTAd21MiKWk1O/ucNPKrnqNUeo7y9V4er0xCtJGrSjFqZ58qpQYBg7TW852PrwLQWs+N1nu0kmdXirk+H3/tP+ALjx3+cbbPx4kd7CL85yC5L228rTvmwo9QKUe9B2Jq1ql9Gg4+H49IQrSVq0oxmivFQcD0KG6vvfabDiBEc8K+9KrSXpOObclzU/w9h6EyVsY6kxDt4KpSPOpKUSk1BzgByAL2AG8CXbXWDzirwV5ALTAL6KSUmg5c6Lx8jFLqHec5F2mtVyml/giMxd69eSXwTWCM8+uz50Xha3PV/yjhHuuOuXhZS1aJh/gzJtWEa+fHMJEQ7eKq77UtXSm+p7WeBpQBDcB5zuMXAH/TWj8K3ALM1VpP11rvcT4/ETgDeAA4Vyl1DpChtZ4CzAN+1tzz2vk1HbLn6E8RIr7CvowDu3oeP7o1r5F5qCLBebIUlzn//RToBmxUSk0DUrTWO77idS9orRuBbUAaMBJY4nxuMRA4wvOiQUpRJJy1wy/5BKU6t+Y1zjzU4lhlEqKdPFmKxzv/HQdsAJ4FngFeaPKcWuxdrCj12SDNw68IXw0cGl01yfm4uedFw94YbFOINmv0ZVaW9Zgwti2vTc2aPgKZhyoSkydLcaJSaj7QCSgE3gZSgX80ec4nwAil1HvAxc1tRGv9OlCrlHofOB/4bRtzH1WgONSIhy/LEImneMR3VqBUblte68xD/SjamYSIAleVYksvyXi4yaUWXbCL8a9a689WeFrrBuyTZr7Eee185/c3Hfbpuc09L0p2YRe5EEY1+rMq9nQfO6492/BnTstsrH41WpGEiBZvlaLWes5hH+8DToxVoCjbBBxxjJYQ8RIacflK7OPwbeZLGza2sdq3ESJDj/5sIeLGVYeq3DzRBkAGKgvjGlI77NvbbfRx0diWzEMVCaYOcNXfSSlFIWIslHdlEUrlRGNbzjxUmd4uEsWG2QWFrrqhu5SiEDHUkJqzt7zLqAnR2p4zD/WTaG1PiHZaZzpAtEkpChFDawJXrkapqE5bT806tU80tydEO0gpJpnN2DfAFCLu6tM67tnXOTAx2tt15qGuiPZ2hWgDKcVkEigOue4gsEgeawJXrUGprFhs258xuTYW2xWilaQUk5DsQhVxV5fWqWx/p+EnxGr7vvQxE2UeqkgAUopJSEpRxN2akVetRamMWG1fqRR/SuqwUKy2L0QL7J9dUOi6GdNSikJEWV1659KK3GExWyUekpo1PQ+ZhyrMWW86QCx4oRRdt7wXiW3VyKvXo1R6rN/Hnofa6eNYv48QR+DK761eKEW5pkvETW1G15IDHQdPOvozo8OfOTVmu2iFOAopxWQUKA5tBPabziG8YdXIqzehVLTuCXpUvrRhY8G3IV7vJ0QTUopJbNnRnyJE+9RkdttxMGdg3FaJh/jSx+6M93sKAawyHSAWvFKKctxFxNyqkddsQanUeL+vP3OSzEMV8VYJrDEdIhakFIWIgurMnlursvvHfZUIh+ah9pFj5yKeFrttEPghUopCRMHqUVdvR6mW3rQ76lKzTu1r6r2FJ31oOkCseKIUA8WhrYDrLjIViaE6q9eWqg59J5vMkOLvMRSVsdJkBuEpH5gOECueKEWHnGwjYmLVqGt3opTPdA5/xuQa0xmEJ1jAEtMhYsVLpSi7UEXUVXXos6k6q5eRY4mHc+ah7jKdQ7je6tkFhQdMh4gVKUUh2mHVqGt2oVRC/Duy56Ees9Z0DuF6rj2eCN4qRdcu94UZB7P7b6jJ7JkQq8RDUrOmjUDmoYrYklJ0g0BxaBew2nQO4R6rRl69N1FWiYc481A/Mp1DuJprT7IBD5Wi4y3TAYQ7HMgZuK42s3vM74TRFv7MaZmmMwjXKp9dUOjK8W6HeK0U/2s6gHCHVSOv3odSynSO5vjShso8VBErrt51Ct4rxQVAg+kQIrlVdBxcXJfRNSFXiYfIPFQRI4tMB4g1T5VioDhUg8v3h4vYWz3y6gOJuko8ROahihh5w3SAWPNUKTpkF6pos/25w9bUZ3Q53nSOo5F5qCIGts0uKHT91CQvlqKcbCPabM3IWdWmM7SUzEMVUfaa6QDx4MVSXA6Umw4hks++TsNX16d3mmg6R0s581BXmM4hXONV0wHiwXOlGCgOWcD/TOcQyWdN4Ko60xlay58xOekyi4R0EJhvOkQ8eK4UHbILVbRKeefApw3pueNN52gtX/qYCTIPVUTBm7MLCj1x5r5XS/ENwJU3yBSxsSYwM2I6Q1s481CLTecQSc8TxxPBo6UYKA6V4IHrbUR07O167MrGtJxxpnO0VWrWtADQaDqHSFoR4HXTIeLFk6XoeNF0AJEcQnlXJPVeBZWS01OldJK7xIi2+nB2QaFnTk70cim+jP0TkBBHtLvbmBWNqdljTedoL5mH2ryKmlrTEZKBJ846PcSzpRgoDpXhkbOpRNsVj/iOK/6N2PNQ/etN54iHg3X1PP6OPbhqx/5K/m/+Yh773wfMX7sJgH9/WsxfFixBa82G3Z5ZALWHZ44ngodL0VFgOoBIXGXdj1sWTu0w2nSOaPGljyk1nSHWahoaeXHpShrC9k6gfy1fzcXHj+HGUydTtKOU8qoaquob6Nc5l5KKA3TKkgX0UayfXVDoqRO1vF6K/0BOQBBHsHbEpWmmM0STF+ahpii4fNI40lP9ANQ0NNApKxOlFFlpadSHw4AmojWb9uxjaPcuZgMnvudNB4g3T5dioDhUDrxtOodIPLt6TPw47M8Kms4RTV6Yh5qRmkpmWupnHw/q1oX3129h+dYS9tfU0js3h14dc9hfXYsC/vTuh5QdcPXPCe1hAc+YDhFvni5Fh+xCFV+ydvglWaYzxILX5qFeMD5Ij47ZLNqwhVPyhqCUYuqIIYwf1Jc0v49g316Edu42HTNR/W92QeE20yHiTUoR/gXUmw4hEkdpr0lLI/6MkaZzxII9DzXTM/NQU1IUPXI6AHDcgM9/HqhraCTd78fnS5EpHkf2tOkAJni+FAPFoUo8cI8w0XLrjrkox3SGWPJnTPLUdQhvFK1lxujAZ7fA3HOwit6dOtK/SycWrd/CEDmu2Jx9wD9NhzBBaS0/J4XyAufgsdOORfNKep+0dO2IyxL+fontobUVrq94dA/o3qaziIT12OyCwptMhzDB8ytFx7+BLaZDCLM06PXDzs81nSPWnHmo60znEAntr6YDmCKlyGe3k/o/0zmEWSV9pi6xfOkjTOeIh9SsaXnI5UiieZ/MLij0zHHnw0kpfu6vyAk3nqVBbxj6ra6mc8SLzEMVX8GTJ9gcIqXoCBSH9gD/z3QOYcaOfqcstnxpx5jOEU/+zGmuvOxEtEsdHrxgvykpxS/6k+kAIv40yto45Js9TOeIN1/a0DFemYcqWuxfswsK95sOYZKUYhOB4tCHgKsnfogv29b/tA+tlNShpnOY4Esf6/p5qKJVPHuCzSFSil/2hOkAIn40KrJp8Dl9TOcwxZ856TjggOkcIiGsnF1Q6Pmxl1KKX/Y8UGk6hIiPrQO+vlinpA42ncMUpdKylb+PZ880FF/wgOkAiUBK8TCB4lANkG86h4g9S6WENw86u5/pHKalZnprHqpo1kbkRENASvFIHgXCpkOI2Noy8MzFOsU/0HQO05x5qHIs3dt+O7ugMGI6RCKQUmxGoDi0Cfi76RwidiyV0rh1wJmeL8RD/BmT5Rpd7yoF5poOkSikFI/s14D85ORSmwfNWKxTfP1N50gUvvTRE0DJmaje9PvZBYXyQ5FDSvEIAsWhdci9Fl3JUr6Gbf2/5tmTa5rjzENdazqHiLsK4M+mQyQSKcWvdi/23aeFi2wa/I0lOsXn+RNsDpeaNX0kMg/Vax6fXVB40HSIRCKl+BUCxaEQ8LLpHCJ6LOWv39b/1GGmcyQilZLdQ+ahekot9kmFogkpxaO7B+Tm3G6xccg3l6B8ch/BI/BnTpd5qN7x1OyCwj2mQyQaKcWjCBSHVuHRO1C7TSTFX7e93/ThpnMkMl/aEJmH6g2NwO9Mh0hEUootczeyWkx6G4Z+eykqpZfpHInOlz52l+kMIubyZxcUbjMdIhFJKbZAoDi0EnjNdA7RdpGU1NqSPlPyTOdIBv7MSeOQeahuVgXcaTpEopJSbLk7kesWk9b6YecvRaV47vZQbWHPQ+0rE27c64HZBYWyN+AIpBRbKFAc+hR4ynQO0XqRlLTqnb1PGmk6RzJJzTxFLllxp23AQ6ZDJDIpxda5E7mDRtJZd8xFH6NSupvOkUxkHqpr3Ta7oLDOdIhEJqXYCoHi0B7sk25Ekgj70qtKe00Kms6RjGQequssnl1Q+ILpEIlOSrH1/gisMx1CtMy6Yy5ehlJdTOdIRjIP1XV+ZDpAMpBSbKVAcagRmG06hzi6sC/jwK6ex482nSNZ2fNQh8sPgO5QMLug8EPTIZKBlGIbBIpDhcCbpnOIr1Y8/NJPUKqz6RzJLDVrWgCZh5rs6oCfmQ6RLKQU2+5W5EbECavRn1m5u8f4saZzJDt7Hmrnj0znEO3y+9kFhVtNh0gWUopt5AwLf8J0DtG84hHf+QSlck3ncAN/5rRs0xlEm5UB95sOkUykFNvnV0C56RDiixr9WRV7uo09znQOt/ClDRkt81CT1h1ya6jWkVJsh0BxaD/wE9M5xBeF8q5YiVIdTedwE1/6WDkLNfm8M7ug8GnTIZKNlGI7BYpDzwBvmc4hbA2pHfbt7RocbzqH2/gzJx2HzENNJjXAd02HSEZSitFxHVBtOoSAUN6Vn6KUHAOLMmce6grTOUSL3Tm7oHCT6RDJSEoxCgLFoS3Az03n8LqG1Jy95V1GTTSdw61SM0/pazqDaJElwCOmQyQrKcXo+SPwgekQXrYmMHM1SnUwncOtZB5qUmgArp1dUGiZDpKspBSjJFAcsoCrsPflizirT8vdva9z3vGmc7idzENNeHNmqmXmgwAAEXBJREFUFxSuMh0imUkpRlGgOLQeuN10Di9aHZgZQqlM0zncTuahJrQPgQdNh0h2UorR90fgXdMhvKQurVNZRafhJ5jO4QXOPNS1pnOIL6kBZs4uKJQbobeTlGKUBYpDGpiFnL4eN6tHzlqLUhmmc0TT/qrdpiMcUWrWtJHIPNRE87PZBYUyYCEKpBRjIFAc2gr8wHQOL6hN71JamTs0KVaJB2r28ftXbv7CYzv3beaPhfb8h1eWPMVjr/8MrTXrdibu1Q8yDzXh/A943HQIt5BSjJFAceg54CnTOdxu9chZ61Eq3XSOo6mpP8hz7z5Iffjzm55rrfnHh08Qsew9XlV1FfTvdgw79q6nc3YPU1FbxJ81Tc7yTQy7sXebatNB3EJKMbZuAhL3R/4kV5vRteRAx8GTTOdoCaVSuPr0X5CR+nmXLF77H47p0+RGHlpjWRE2lBZxTO8xBlK2nC91yBiZh2pcBLhkdkFhSbQ3rJT6sVLqe00+vlUpNeuw59ynlDrL+f1gpdTz0c5hgpRiDAWKQ3XAhcjxxZhYNfLqTSiVZjpHS2SmdSAz/fNBO1V1lXy0/m1OH33RZ4/17jKYfVVlKKV45NVb2LU/se/240sfu8t0Bo+7Y3ZBYdRP6lNKvQ5cDdyglCpUShUC1wK3KqX+5jzHB5wBLFNKnQdU4ZLjzFKKMRYoDm3APvFGRFFNZvftB3MGJsUqsTmvLnmKc4+/Fp/P/9ljp46+gOOPOZ00fzpjBk9h1bYlBhMenT9z0jjkBz5T/jG7oDAml19orWcAfwFeBx5wfr0NPKy1vtx52kXAGkABlwEa6KeU+o1SqiAWueJFSjEOAsWhfyBjl6Jq1chrtqBUqukcbbW+dCX/WvIkj7z6I0rKN/DaUvtmBrUN1aSnZuH3paJ1Yg8lceahyoSb+FtHfH7QzgCynV+fHbdX9jH8H2MX4WhgIjAX6Ai8DPSOQ7aY8R/9KSJKfgqcAEw2HSTZVWf23FqV3S+p/xx/dcmzn/3+kVd/xDeOv5qyiu307TqE9NQsnnjjDq6Y/lODCVsmNfPU/g0HnzMdw0uqgW/PLiiMxwp9BnDojjP9gMXO748HXsLeffot4BPsmyL8DvsciqS+VlJpLSctxUsoL9Af+y9QV9NZktmSCXcsqs7ue5LpHMJWV/HEJ+jacaZzeMSlswsKX4zVxp1jhRZwM1ChtZ7rPH4LUAEc+mmuC3YJ/hh4DPsStEewC3GP1vrHscoYa7JSjKNAcWh7KC9wOfBv7H3xopWqsnpvru7QJ2mPJbqRP/PE+nDN/0zH8II/xLIQHVcCF2DvGlVKqQucx5Xz2MXAn7BHyjXld55zK+BXSnXVWpfHOGtMyDHFOAsUh/4D/MJ0jmS1atQ1pdg/zYoE4UsLTpR5qDG3CHtVFlNa62ecE20WAKu01udorc8B6oFCrfVZWuvXmrwkAuRiXy95ldZ6H3Ap8MNYZ40VKUUDAsWh+5AL+1vtYIe+G2uyeskqMcEoleJLSR2+znQOFysDLppdUBjzSx6UUhlKqX9iF90dTT51GXCsUmqu83EakKG13o994s+bwBtKqbeBKSTxhB05pmhIKC/gxz7l+eumsySLxcf/8sOarJ5JfYKNW2mrand95V86A0l7RnCCqgNOn11QuCheb6iUGqS13nKEz/XUWpfFK4sJslI0JFAcCmPvu//UdJZkcCC7//qazB5JMePUi2QeakwcmlgTt0IEOFIhOp9zdSGClKJRgeLQQezTnqM+psltVo+6phyl5O9rAvNnTcs++rNEK1w/u6DwFdMhvEa+yRgWKA7twC7Gg6azJKrKnEFrazO6ySoxwflSh4yWeahR8//bu/8guev6juPP7+3lkgu/1RRNABMk5r7RAIaEFKb5wVlUKthaplZqHaEdpmcPbXU70ql2tK3IjLVaf9zMAY4z2h90sVNqZ+mMHXoQtZIUiiSafBcMSZAkDkRCgCTNXe52+8f3y4/mx5G7293Pd3efj5mdvb373n5fk5nM677f/X7fnz8vlspedxCApZgDcSXZRDojdTx0ljzasvSG/USRt7C0gMLst3kV6swNFUvlz4YO0aksxZyIK8l3gT8MnSNv9p9+fnK416PEVtHdu+oSnIc6E9+mhW9naAeWYo7EleQO4JbQOfJky9IbDoTOoJMXRT2nOA912u4DPlgslfM99LbNWYo5E1eSTwFfDJ0jD549Y/HW0TmvWRk6h6ZmVm//uaEztKBHgN8olsqjoYN0Oksxh+JKUsRVNdiy9PpDoTNo6rq6551P1Ptw6BwtZAdwVZOGfOtVWIo5FVeSjwFfDZ0jlH1nLvnJ2OwzV4TOoenp7r28LRacbYKngHcUS2UXbM4JSzHH4kryUdLhux1n69Lrx0Jn0PQVepatcB7qq9oFrC2WyttCB9HLLMX8uwm4LXSIZnrmrHjzWM/py0Pn0PSl81CXPBo6R45tB1YXS2X/jXLGUsy5uJLUgA8Dd4TO0ixb4w+19CKlSs2au+YtgKdRj1UB1hRL5Z2hg+hYlmILyIrxD4BvhM7SaHtfu+yRIz2nuWBtG4i6Tp3nPNRjbCI9Zepox5yyFFtEVow30ubFmPT9rpNr2kj33HXOQ33ZRuCKYqn8dOggOjFLsYXElaQaV5LfBz4XOksjPP26i340PuvUi0LnUP0UZi26ELpdazFdtPfKYqn8bOggmpyl2ILiSvJJ0s8Z2+qzt8qSDxRCZ1D9FWa/re2XG3oV3yW9D9Gh/y3AUmxRcSUZBq4F/jd0lnp4at7yh8dnnXJh6Byqv+7eVcuB50LnCORfgfcUS+W2+H/aCSzFFhZXku8AbweeCZ1lpipLfqcndAY1RjoP9ZxHQucI4FvAbxVLZe+5bSGWYouLK8kDwOWko6Ja0s/PvvShie7et4bOocaZNfeK80JnaKIqcHOxVP5QsVR2ObgWYym2gbiSPAZcBrTkvMnHFv/23NAZ1FhdhXmLOmQe6vOkp0s/HzqIpsdSbBNxJXkKWEv6oX7L2PP6X35wonvO0tA51HjdvZe3+2nEx4HLiqXyPaGDaPosxTYSV5IDwNW0yAobNeCxxe87LXQONUehZ9nKNp6HOgJcWiyVt4YOopmxFNtMXEnGsxU23g8cDJ1nMnve8Csbq4XZfaFzqDnaeB7q14B3FkvlfaGDaOaiWq0WOoMaJOmLlwL/AiwJneVoNaitX/2ln1YLPW8OnUXNU6se2Dv63O1nAO1wtfER4KZiqXx76CCqH48U21hcSbYCK0mLMVd2z1+z0ULsPOk81Nc8FDpHHfwC+FULsf1Yim0uriQvxJXkWuBmcjIBp0ZU3fam974udA6F0T13bat/jvwIsLJYKn8vdBDVn6XYIeJK8nngSiD4MOInz7liY7XQc0HoHAqjMGvRshadh1oD/hpY5bJP7ctS7CBxJbkPWA48ECpDjai6/fz3/FKo/SsfCnOWt9o81F2kp0s/4YSa9mYpdpi4kuwG1gCfIcACsD879+0bql2z3tTs/Spfuudc2krzUL8NXFgslUdCB1HjefVpB0v64uWk8xnf0oz91Ygm7l/ztz+rdXUvasb+lG+jL9y1vja+a23oHJN4AfhIsVT+Zuggah6PFDtYXEkeBi4h/Zyk2uj9PXHeOzdYiHpRNg81r3+VPwBcbCF2Hkuxw8WVZDSuJJ8AVgPbGrWfatQ1vmPhVec06v3VetJ5qHN/FDrHUcaBTwOri6Xy9tBh1HyWogCIK8kPgYuAIRrw1/vON75rQ62r+431fl+1tu7ey5r+ufYkHictw78slsq5uH1JzWcp6iVxJTkUV5KbgHcAT9brfatR15EnznuXhahjFHqWrYBoT+AYY8CtpBfTbAicRYFZijpGXEnuBZYBX6EON/zvWHj1hlpX4dwZB1PbiaKuQlfPkpD3LN5LWoZ/ViyVDwXMoZzw6lNNKumLlwFfJV2WasqqUWFs/eov7a11FRbUN5naRaB5qLuBjxdL5buauE+1AI8UNam4kvw4riTrSFfd2DXV39++6JoNFqIm0+R5qEdIr7busxB1PB4p6qQlffEpwCeBIifxV3016h69f80X9xEV3tDwcGppE0d2bD5y4O4LG7yb+4DBYqmcNHg/amGWoqYs6YsvAL4M/Npk2z12wbXf23VO/5rmpFKrO/zsVx6F8UYsc7YH+JNiqXxnA95bbcbTp5qyuJJsiyvJu4FrSC9jP8ZEV/fhXQvW5W4dR+VXYc7yeg+rPwx8gfRUqYWok+KRomYk6YtnATcCnwJeOk366OL3rd+9YG2eR3gpZ2q1sYOj+782Dpwxw7caBW4Hbi2Wyj+feTJ1EktRdZH0xb3ATcDNE109vetX/81Boq55oXOptYy9cNf66vTnoY4BXwc+VyyVd9cxljqIpai6Svri0x9fePX1Tyy86i+AM0PnUWupTvxix9jz31oIRFP4tSPAN4BbiqVy3YZOqDNZimqIoYGRM4CPAh8DzgocRy3k8P7hh6kdWn4Sm44D3wQ+66K/qhdLUQ01NDByGvAR0oI8O3ActYDx0c0bxw/du2qSTSaAvwP+yqHdqjdLUU0xNDAyG7gO+GPSwePScdVq1YnR/V9+Cmrzj/rRIeDvgS8US+WfBoimDmApqumGBkauID2t+m68LUjHMXbw3++vjlXWZS+fIF295evFUvnZcKnUCSxFBTM0MHIB8EfADcApgeMoR2rVg3tHn7vtx6Rl+B2XclKzWIoKbmhg5Ezg90jL8a2B4yis50g/LxweHO7fEjqMOo+lqFwZGhhZQVqO1+FVq51kA+k9hncODve7hJOCsRSVS9mFOb9OWpBXAoWwidQAjwL/APzj4HD/cccFSs1mKSr3hgZGFgAfBD6Ap1db3R7gn0iL8H9Ch5GOZimqpQwNjCwG3ps9VjG1yScKYz9wN+lR4X2Dw/3VwHmkE7IU1bKGBkbmk55i/U1gHdAdNJBeaStwD1AGfjg43D8eOI90UixFtYWhgZGzgKtJ733sBxxG3lyjwP2kJXjP4HD/jrBxpOmxFNV2hgZGItLPHvuzx1pmvhyR/r8qsAX4PvAfwL2Dw/0Hw0aSZs5SVNsbGhgpAJfwcklejsMCpmoMeJC0BH8A/NfgcP/+sJGk+rMU1XGGBka6gBhYQVqWK4CLgd6QuXLmSWAT8ABpCf734HD/4bCRpMazFCVeOppcSlqQL5bkEuC1IXM1wSHgJ8Dm7LEJ2OxRoDqVpShNIruA583A4lc8v/g4PWC0qThIOlR7J7Aje95OWobbvEVCepmlKE3T0MDI2cB84PWka0W++Hz2Ud87i/rfTzkG7AOeyZ73veL106TFtxPYOTjcv7fO+5balqUoNVh2Nexc0ot7Ts2ee4HZr3j0kC6ee4R0RfnxE3z9PLDPKz2lxrAUJUnKuMCrJEkZS1GSpIylKElSxlKUJCljKUqSlLEUJUnKWIqSJGUsRUmSMpaiJEkZS1GSpIylKElSxlKUJCljKUpTEEXRxVEUXR86h6TGcJUMSZIyHilKUxBF0booij4TRdH8KIp+EEXR96MouiX72R1RFF2cfX1bFEUrj7edpPyyFKXpWQD8KXAVcE32vX/OXgP01Wq1B0+wnaSc6g4dQGpR48CngQPAadn3/hP4cBRFMfDQJNtJyilLUZqejwO3ApuAzQC1Wm08iqK9wHWkR43H3U5Sfnn6VJqaXmAUKAPDwL8Bh6IoWpD9/G7g/cCG7PWJtpOUQ159Kp2EKIoWAncCE8CNtVotCRpIUkNYipIkZTx9KklSxlKUJCljKUqSlLEUJUnKWIqSJGUsRUmSMpaiJEkZS1GSpIylKElSxlKUJCljKUqSlLEUJUnKWIqSJGUsRUmSMpaiJEkZS1GSpIylKElSxlKUJCljKUqSlLEUJUnKWIqSJGUsRUmSMpaiJEkZS1GSpIylKElSxlKUJCljKUqSlLEUJUnK/B/+uPVozYf2cwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['pl_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的编程语言')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 大数据工具 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_151f624c_8b6b_11ea_84ab_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >bd_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow0_col1\" class=\"data row0 col1\" >flink</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow0_col2\" class=\"data row0 col2\" >35333</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow0_col3\" class=\"data row0 col3\" >37692</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow0_col4\" class=\"data row0 col4\" >17500</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow0_col5\" class=\"data row0 col5\" >40000</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow0_col6\" class=\"data row0 col6\" >15</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow0_col7\" class=\"data row0 col7\" >4.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow1_col0\" class=\"data row1 col0\" >3</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow1_col1\" class=\"data row1 col1\" >hive</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow1_col2\" class=\"data row1 col2\" >30843</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow1_col3\" class=\"data row1 col3\" >30000</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow1_col4\" class=\"data row1 col4\" >10458</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow1_col5\" class=\"data row1 col5\" >56125</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow1_col6\" class=\"data row1 col6\" >51</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow1_col7\" class=\"data row1 col7\" >14.87%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow2_col0\" class=\"data row2 col0\" >4</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow2_col1\" class=\"data row2 col1\" >storm</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow2_col2\" class=\"data row2 col2\" >28235</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow2_col3\" class=\"data row2 col3\" >27500</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow2_col4\" class=\"data row2 col4\" >17500</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow2_col5\" class=\"data row2 col5\" >40000</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow2_col6\" class=\"data row2 col6\" >17</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow2_col7\" class=\"data row2 col7\" >4.96%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow3_col0\" class=\"data row3 col0\" >5</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow3_col1\" class=\"data row3 col1\" >spark</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow3_col2\" class=\"data row3 col2\" >27712</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow3_col3\" class=\"data row3 col3\" >27500</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow3_col4\" class=\"data row3 col4\" >10688</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow3_col5\" class=\"data row3 col5\" >51682</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow3_col6\" class=\"data row3 col6\" >142</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow3_col7\" class=\"data row3 col7\" >41.40%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow4_col0\" class=\"data row4 col0\" >6</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow4_col1\" class=\"data row4 col1\" >hadoop</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow4_col2\" class=\"data row4 col2\" >26755</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow4_col3\" class=\"data row4 col3\" >26389</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow4_col4\" class=\"data row4 col4\" >10000</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow4_col5\" class=\"data row4 col5\" >54409</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow4_col6\" class=\"data row4 col6\" >102</td>\n",
       "                        <td id=\"T_151f624c_8b6b_11ea_84ab_701ce71031efrow4_col7\" class=\"data row4 col7\" >29.74%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25eb596bec8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'bd_')\n",
    "data_pl=data_pl[data_pl['head_count']>10]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z5HTgPuALxCPfBH5AvFFvtVB5o4vqqrxIRGOhRDR2LfAC8Hu0AHulvYqymrfzmqbm5m5uOhb4MrCLeOSrxCOn5DGW8jEtQZVTiWisJhGN3QpsAX4KTLccqaS1VZnyGb4t0npdY/PsHj6rP/AhYCvxyIPEI3PzkEz5mJagyolENDYkEY19CtgBfAPQd+450FJN2ZTglFR6VT+R3t74HwTeAqwgHnmIeKSs5odV9ug1QdUniWhsNPAR4CbKeP5OW1qry2cA0QfrG/rnaFdXApcTj9wHfIp449Yc7Vf5kB4Jql5JRGP9EtHYZ/DWn7sNLcC8aAmXx/doSGTHee0dxxsQ01MB4J1AHfHI3cQjJ+dw38pHyuIbTBVOIhoziWjsOmAj8N/oDe551Rw2QdsZcuHC1rbtedp1CLgZ2Ew88nXikWF5eh1VprQEVbclorH5wDPAT4DRluP4QkuYCtsZ+kzEvbW+Id+rflThnZF4kXjk08Qj+uZMdYuWoDqhRDQ2NhGN3Q88gXcflyqQ5moqbWfoq8Guu3pMxinUm6Z+wCeBBPHIFQV6TVXCdGCMOqZENFaLd5P77UC15Ti+1BI2VbYz9NUNjU3tFl72FOBXxCN/Bm4l3rjJQgZVArQE1WtkZ3m5Afh/wEi7afyttbrEr7mKNL69qcXmvX0XA2uJR74GfJZ4Y6vFLKoI6elQ9SqJaOwc4Dngf9ECtK61mhrbGfpiRjK1plrEdpFXAh/FG0l6teUsqshoCSrAO/WZiMa+C/wLmGc7j/K0VZX2rSe31TcMsZ2hkzHA/cQjfyMemWw7jCoOWoKKRDS2CFgLvA9d3aFoCDipClOyR4KVrmw9syNZjDO7LAJWE498kHhE/7/7nJagjyWisf6JaGwZ8FdgnOU46rVabAfoize0tu60neE4aoBvAn/Tybn9TUvQpxLR2GK8o7/3okd/Rck12BhVmRsizpL6xqm2Y3TD+XgDZ262HUTZoSXoM4lorDoRjX0d+As6yXVRcwO02c7QW8MdZ+UwxymV2Vv6A3cTj/yReEQngfAZLUEfSURjM/FGft6GHv0VvUyQDtsZeuumhqZSXAfxEmAd8ci1toOowtES9IHsfJ+3A8+i6/uVjFSIpO0MvWFEDl/ZbPXewL4YCPyUeOQXxCMDbIdR+aclWOaySx39FW+V7pKffcRPUiFStjP0xryO5NpKSn66t6uA54lHZtoOovJLS7CMJaKxc4GVeEPCVYnpqCRjO0NvLK1vGGE7Q45MBp4mHrnedhCVP1qCZSoRjd0C/A0olcEJqou2Kkruulq1626clUxNsZ0jh8LAPcQjPyQe0flzy5CWYJlJRGMV2Xv/vgdlsAyPj7VVGdd2hp66srl1r+0MefJu4CnikYm2g6jc0hIsI4lobBjwd7x7/1SJay214w6R1C0NjTNsx8ij2cAK4pHLbQdRuaMlWCYS0dg84HngHNtZVG40h0vrNpYxmczKQa472HaOPIsAvyYe+bxOuVYetATLQCIaewfwGHCy7Swqd1qrCdrO0BPvbWjy08+TO4EH9Dph6fPTf9qyk4jGAolo7EvAz6HE151Tr9EcNiVTggGR/W9safXb6iNvAf5OPDLUdhDVe1qCJSoRjQ0ElgN32M6i8qMlXDoDm85u79gQpLSOXHPkbLzbKAo6ItYYEzfGLDzK418vZI5yoCVYghLRWBRv9pdLbGdR+dMcLp0bzpfWN/j5VPwEvJGjC2wHEZGltjOUGi3BEpOIxuYCj+PdyKvKWEu1KYkZfvq57vpoKu33WwcGAX8hHrmmgK95oTHmX8aY1caYEQDGmEePfNIY8wljzOXZjz9mjHmLMabGGPPL7PO+U8CsRUtLsIQkorH5eLdAFNNq3SpPWqtL4zrv1U3Nh2xnKBKVwM+IRz5VoNebJCILgF9x9FmhfgFcmv14AfAH4D3AuuzzRhpjfD8tnJZgiciu/v4I3hBt5QNt1RT/qvIi7e9ubJplO0aRiROPfLcAt1D8NPv7SxxlrlYR2QSMMcYMABpEpBWYAlyRPWKcAPh+6SgtwRKQiMZejzcIpp/tLKpw2qqotZ3hRManM6sGuKJvzF7rfcAPiEfy+TO2tRvbPAssBX6X/fNG4OsishD4L7wC9TUtwSKXiMb+A/g1oPcj+YhAJh0yRf9v/v6GxpK4bmnJu4GfEI/YHDX7C7wSfDj75x8Alxpj/gXcAuy0FaxYGBGxnUEdQyIauxb4Mf4ceu5rAg1Xfyw00HaO4wmKvLxy+84RAX0zfSK/AN5BvLEkVwUpd/qft0glorH3AD9BC9CXXNOtU11WndfWvkkLsFveAvySeKRkbnnxE/0PXIQS0dhS4PtQWnNHqtxxAnTYznBcIrK0vmG87Rgl5M3Ab3SateKjJVhkEtHYfwFfs51D2ZUJFncJRlz3hfHpzCm2c5SYS4GHiUeKf9Svj2gJFpFENPZZ4LO2cyj7UiGStjMczzVNzc22M5SoxcDviUd0QFGR0BIsEolo7Fa8IctKkawgZTvDMYm0XNfYPMd2jBK2CLjf8qhRlaUlWAQS0dhV6ClQ1UlHJUU7kjCaSq+uEdF7VvvmcuB/dU1C+7QELUtEY+cC/4f+W6hO2qtwbWc4lg/UNwywnaFMXAfoqg+W6Q9eixLR2FTgt4BeH1Cv0lplirIEQyI7zmvv8P18kzl0K/HIJ2yH8DMtQUsS0dgo4E94s88r9SqtRTqQ/qLWtm22M5ShzxGP3Gg7hF9pCVqQiMYGAH8E/LwGmzqOlnAR3iMq4t5a33Cq7Rhl6m7ikctsh/AjLcECS0RjlXhzgeopJXVMzWFTdCMHhzjuqtEZZ5TtHGUqBDxIPHKG7SB+oyVYQIlozODNBXq0tb+U+reWMCHbGbq6obGpqO9dLAM1eLPK6BuNAtISLKwvAO+wHUIVv5bqIitBkca3NzfPtR3DB0YCv9ab6QtHS7BAsjfDf8R2DlUamsPFNWJ4ZjK1pkp0Oa8COQO423YIv9ASLIBENHYRejO86oHWcHGtJXhrfcMQ2xl85jrikQ/bDuEHWoJ5lojGRqM3w6seaqkibDvDEZWubD2zIznNdg4f+iLxyEW2Q5Q7/cGcR4loLAjcBwy1nUWVlrZqimalgTe0tvp+9XFLgnhzjE6yHaScaQnm1+eAc22HUKWnvYpa2xkAEMksqW+cajuGjw0Cfks80t92kHKlJZgniWjs9cBHbedQpUcglQmanKxC7rQ7OG1Or58/3HFWDnOcYbnIonptKvAz2yHKVXENwy4TiWhsDPBTdGV41QsCLcDgbm/vCJs+somKoRUADJg3gPpH6znl9lNo39JO/3m9P4i4uaGpKOcw9aE3E498gHjjt20HKTd6JJhjiWgsBDwA6Gg61StugLaebN+xs4PIWREmfGwCEz42gdTeFMPfOpz2Le2IIwRCvfs2NyKHrmxumderJ6t8+BLxyAzbIcqNlmDu/Q8w33YIVbqcAO092b5taxvNq5vZ+umt7PrRLjAgKaF1Uys1U3o/vua0juS6Cqjo9Q5UrlUD9xGPFM3I4XKgJZhDiWjsDcAdtnOo0pYJ0qPpycLjw4z7z3FM/NREcCA0IMShvx6iclgle+/fS8NTDb3KsbS+YUSvnqjyaRrwFdshyomWYI4korGxwE/Q64Cqj1KhnpVg9cnVVAz0DtjC48MEKgOMee8YKgZXUDm0kpYNLT3OEHbdjTOTqSk9fqIqhPcRj7zZdohyoSWYA4lorAJ4kB4MZlDqWJIVpHuy/a67d9H+UjviCk0rm6geW03bxjZqJtdgggZjev6+7Irm1r09fpIqpB8Rj4y2HaIcaAnmxmeAM22HUOWho5JMT7Yf9uZh7Lp7F1s+uYWaiTX0i/XDVBpCkRBtW9qoGtPDaUhFUrc0NOoAjOI2BPgp8Yj+DO8jvUWijxLR2Gz0OqDKobYqenRjX/WYaiZ/bvKrHoucFgFg/EfH9/j1x2QyKwe57lk9fqIqtEV4k/J/wXaQUqbvIvogOy3aD9A3EyqHWquN2Hz9Wxqaim5BX3VMnyYeidoOUcq0BPvmVuA02yFUeWm1uH5EQGTfG1padd3A0lEF3E08ogPyeklLsJcS0dgpwGdt51Dlp6Xa3vfl/PaORNCbuFmVjnOBm22HKFVagr33XaCf7RCq/DSHjbXvy6WHG8baem3VJ18kHhlpO0Qp0hLshUQ0djXwets5VHlqDtu5xlzruuunpNMTbLy26rMI8E3bIUqRlmAPJaKxQcA3bOdQ5aslTE5WkOiptzY1H7LxuipnriIeeZPtEKVGS7DnvgQMtx1Cla+WMD28sS8HRNrf3dg0q+Cvq3LtO7r2YM9oCfZAIho7D3iX7RyqvLVWm4KX4IR0ZuUAVyKFfl2Vc2OAz9sOUUq0BLspEY1VAd9H5wZVedZaTcFXCVhS36ArE5SP9xGP6K1b3aQl2H2fAHRCYZV3bVWFHXUcFNl9QVv77EK+psqrALrSRLdpCXZDIhqbCHzUdg7lD21V1Bby9Ra2tW8O6M+CcrOAeORy2yFKgf7H755Pg50Re8pfBDrcgCncLRIisvRwg94WUZ6+SDyiiyKfgJbgCSSisRnA223nUP4g0PPF//og4rprxmUyeoN8eZoM3GI7RLHTEjyxz6F/T6pA3ABthXy9a5qaWwv5eqrgPkU8MtB2iGKmP9yPIxGNnQXozaeqYJwA7QV7MZGW6xqbdUBMeRuCN6hPHYOW4PH9j+0Ayl/SIZKFeq1oKr26RkTnvy1/HyQe6fnCkj6hJXgMiWjsAuB82zmUv6RCpAr1Wh+sb9CZRfyhCr2B/pi0BI9NjwJVwSUrSBfidUIiOxa0d+g0af5xNfHIHNshipGW4FEkorErgNNt51D+015JphCvc3Fr27ZCvI4qKnpt8Ci0BLtIRGMBvBGhShVce5Vx8/4iIu6t9Q06+5H/XEk8MtV2iGKjJfha1wD6H0VZ0VqN5Ps1TnLcVaMyji7A6j8G+JjtEMVGS7CTRDRWiTc7jFJWtFTn/zVuaGwq2AhUVXTeTjyiMwR1oiX4ajcB42yHUP7VEs7z96RI49uam+fm9TVUMQuiR4OvoiWYlb0WeLvtHMrfmsMmmM/9z0ym1lQJBTjeLLx9LS5pJ+9nk8vBdcQjY2yHKBZagq94PaCnCZRVzWHyOnn2bfUNQ/K5/1zb1+Jy7o+9md12N7mM+WozC+9pZeE9rRxodfn2sylO/0ELrSnhz1szVAR1uc9uqAT+03aIYqEl+IoP2g6gVEs4f6uVVLqy9YyO5LR87T/X6tuF63/TTmvKO7p7ZrfDJ86t4tEb+vHoDf0Y2i/A6r0ON82p5LmXHfpVaAH2wE3EI8NthygGWoJAIhqbAlxoO4dSLdWmKl/7fmNL66587TsfggF44KoaBlR55fb0Locfrkox9/stfPxvHQCIQNqFR7ZmuHRy4VagKgNh9I0/oCV4xAfwhg8rZVVrdZ6u14lkljQ0lNStPwOqDJHqV74tL50U4tHr+/Hczf14apfDC/scLpoY4uFNacYMCPCm+9r4x7aCzDVQLm4iHvH9Oqm+L8FENNYfuN52DqUAWqsJ52O/Ixxn5VDHHZqPfRfK/JOD9K8yBAOGOSOCbD7kcvX0CuILqxhYbbhscoiHEgWZda5cDAf+w3YI23xfgsANgE4krIpCWxU1+djvzQ1N+Z+JJs8u/r829jS7tKWFR7ZmmD7M+/G1+ZDLxEGGqpDB1cGhPfU+2wFs83UJJqIxg3cqVKmi0FFJba73aUQOXdHcMi/X+y20T51Xxfk/aeOsH7Zyy2mVTDkpSFNSGFEbYOrQIHevSHHBBL0u2EPnEo9Mtx3CJiPi37dOiWjsYuBPtnMoBSDQdvXHQjk/Ejy9veOf/7t3/3m53q8qG98j3vh+2yFs8fWRIDo6ShURMbTmY79L6xtG5GO/qmxcQzzi20tCvi3BRDQ2EbjUdg6ljnANbbneZ9h162YmU7pihDqe/ngLB/hS0ZagMWahMSaex5dYQhF//cp/MkHac73PK5tb9uV6n6os+XaAjC9LIBGN1QA32s6hVGfpILld3UEk9d6Gphk53acqVzOIR86xHcKGnA6lMsaEgV8AA4BDwHpgHlADHADeBiD6bN8AACAASURBVAjwM+AU4CBwlYikjTGPAr8FbhSRmZ32ORJ4EHibiOzOUdTLgYE52pdSOZGqIJXL/Z2cyawY5Lpn53KfqqxdAzxuO0Sh5fpIcCrgisgC4MdALfCYiJwH7APeDAwBlgPnAU3AkWVdRgLSuQCzz/8d8N4cFiDA23O4L6VyIllBTqc7uaWhUe8XUD1xFfGI7/7P5LoEVwLrjDGPABcDbcCK7OdewFurLw28Ae+IcQL8e4aMRuCbXfZ3E7ANyNl9LIlobFA2m1JFpb0ydyUYENl3WUubrhuoemIIPpxDOdclOAt4QkQuAgYB5wJnZD83B9gCXAmsy/7e+eiuTUS6zmqxDHgX8CFjTEWOMv4HkKt9KZUzbVUmZ7O6zG/vSAS9BVSV6gnfnSXLdQluB241xjwJjACeB07PXu8bCDwMPAFcjXfueTAw+jj76xCRFryjxlyNXvLdP7IqDa3V5GzmiqWHG8bmal/KVy4nHsnL/LXFKqfnf0WkgU6nGrO3OHxVRB7ttFkdMJMuRGRhlz8/Cjya/firuciXiMZGAgtPtJ1SNrRU52Ylk1rXXTclnfb1VFiq1/oDlwG/tB2kUPJ6i4SIxLsUoG1vwae3haji1xLOTQm+ram5Phf7Ub71NtsBCslvhXCl7QBKHUtz2PT9Gp5I+7sammblII7yr8uIRwbYDlEovinBRDQ2FPDlzaCqNDSH+z5ga2I6s7K/iG9+gKm8qMa7l9oXfFOCwJvQ0XKqiLWE6fMq30vqG3w1qEHlzVtsBygUP5XgFbYDKHU8LWFT1ZfnB0V2XdDWPidXeZSvLSIeqbYdohB8UYKJaKw/cIHtHEodT0s1ffqhc35b+1ZDbgbXKN+rAc63HaIQfFGCeEsm9eldtlL51lZF7xfUFZHbDjeMz2EcpS6zHaAQ/FKCb7QdQKkTae9DCUZcd824TEZvkFe5pCVYRhbaDqDU8QhIeyW1vX3+tU3NLbnMo/wrI4Hdq92Jj308/a59k+/8zam28+Rb2c8YnojGxgNjbOdQ6gRaMaZ3JSjScm1jsw6IUb0iQvIgkfV/dk5rvtdZPGaDjJvIK9NZXgRsshgv78q+BPGWbFKqqImhFXp3JBhLpVfViJyb40iqjKUkuGO1TNpxX2ZR+E/uGdPaqTrWiiMXAt8uZLZC80MJLrAdQKkTcQK09fa5H6xviOQyiyo/IrTuYciG5c6Z7fc5i055UUadgrew+YksHHfn8tD2uy7L6VqXxURLUKkikAnQ0ZvnVYhsP7e94zUT0ivVIRVbnnWju+9zFg34mzt3aoqK03uxmwHAmXir/5Slsi7BRDQ2GphoO4dSJ5IOkezN8y5qbduBt1i18jkRGl+SYYnfuvMzD2TOn7iboZOASTnY9YVoCZYsPQpUJSFVQarHTxJxb61vKPvRe+roRJA2qjY+6U7fd6+zaNC/3JlTHYJn5eGlyvp6s5agUkWgo4J0T59zkuOuGpVx5uUjjypOrphDW2Vk3a+cc/mFc96pBxkYBaJ5ftl54+5cbrbfdVnOFn0uJuVegjoyVJWE9krcnj7nxsamXp1CVaVDBKeZmsSj7qxD9zqLhj7jxqJC4HUFjhEBpuAtiF52yrYEs0snxWznUKo72qpMz0pQpPHq5uZjDWtXJcwRs69Oxm5+yFkQ/JVzTrSB/tNtZwJOR0uw5JT1eexc25NOM7Kiz8vZqV5qraZHp5pmJVNrqkRP95cDEdL19F//V2du473O4pGrZdKpwHDbubo4A/iZ7RD5UM4lqKdCgYOZDO/ZtZNfjRvPwUyGpS/v5v/GercHffXAfhIdSe4eM4Zn29p4c0RvN7OlJdyz1R9uq284KV9ZVP5lJLBrrUzY9qBzXtXvnPmxVsKzbWc6gd7cXlESyrkE9V0y8KUD+0mK0Og4fHzPHtrdV8661TsOseoqEskkIyvK+b9C8WsJd38e3yrX3XJ6R3JqPvOo3BKh4wCR9X9yzmi511k8tk7Gltp0jrPH3bm8Yvtdl/V4AFexK8uffIlorAqYYTuHbU+3thI2AU4KBgkCXxk1ig/s3vXvzwvgCDzf1sa1gwZZy6mgOWyC3d32jS2tu8nN/V8qj1IS2rZSJu+8P3N+zZ/c06d1UFXKI3mrgJnACttBcq0sSxA4Fej2D5VylBJh2aFDfHP0aD64exe1wdf+dUyurGJ1RzsjKkJcu/MlPj18BBOrdNlFG1rCdO+CrEjm/Q2NehRYhERoeZkhG5Y7Z3Xc55w/bpuMGg+U0xqPZ6AlWDJ8Pyr0h4cO8bZBAxlwlPI74vrBgxnb0syhjMOFtf35Z2uLlqAlzdVUdme7EY6zcqjjnpHvPKp72qVi87NubPe9zuKBf3fnTE0TKud/m9OB79kOkWtagmXqqbZWnmlr5b76euqSSf577x4+O2Lka7Zrdlz6BQKkRWhzy/Je2JLQEjbdevdxc0NTj+8nVLkjQuMOGf7vqcle5qTJwGTbuQqkLAu+XEsw3zMoFL2fjX1lgvjrX9px1ALcnkoxpaqKfoEAt+zexf87yjaqMFqrCZ9oGyNy8IrmllK+rlRyRJBWquuecKfv+7mzeMjj7oypLoF8TE1WCmLj7lzeb/tdl7XaDpJL5VqCvj8S7OwnnQqx88fjKl85A/fw+AkFzaRerbWamhNtc3pHcn2F3vqTd66Yg1tk1KYjU5MdIhJDf6YABPBmjlmZqx0aYyLAr7P7/oaI/NoY86iILOyy3ddFZOlx9nMDgIjc09MMZVeCiWgsgDcwRqmS0V5JvxNts/Rwgx6q54EIThM1Gx51Zx3+eeaCoc9KNAZmvu1cRepUcliCwCzgSRH5r+NtdLwC7KuyK0G8hSJPeGpJqWIh4CYrzXGPBMOum5iRSunRSI44EtiTkLFbf+GcF/q187pYE7W+v6Wqm6bkakfGmNuAG4GBxphzgLeIyIFjbPvvo0NjTByowJsVbABwSaftpgHfBt4kIs3dyVGOJag/KFSpacH7Zj6m/2hu2Y/+3+41EVKH6b/hr868xnudxaPWyMTJgB5Z91zOzrKJyDeMMWuAhSIS7+HTJ4nIAmPMJ4FF2cdGAj8HLuluAYKWoFLWuYZWjleCIqn3NjTp6vE9lJbgrrUyftv9zvlVy52zppbA1GSlIGdHgn300+zvL8G/by/6ALAK72zg3u7uqBxL0PcjQ1VpcQO0H+/zJ2cyKwa67tmFylOqRGjfz8D1f3TOaLvPWXTyxtKbmqwUFMvtIEcbofpZYDnwI+Ci7u6oHEtQjwRVSckE6Tje529paCzH79OcSEpo20r31J33Oef3e8Q9bVoHVafZzlTmBoy7c/mQ7Xdddsh2kKPoEJGdxpg6Y8ybROR33XlSOX5zaQmqkpIKcczFcQMiey9radN1A7NEaNnNSet/75ydut85f/wOGVFuU5OVgnFATkpQRB4FHu3y2MKjbLew08fxTh/fc5Rtb+1JhrIqwexCuoNt51CqJ1IhUsf63OvaO+qCMKKQeYpNu1RuetqN7bnPWRz5uzt7WobQmbYz+dw4ymgO0bIqQbwLokqVlI5KMsf63NLDDb77P+0KDTtkROI3zuucB52Fk/Yw5FT03t9iUlZH3uVWgrrQqCo5bVU4R3u81nHXnppOl/39ayK4rVTXPebOOHCvs3jIE+70mEtABwIVr3G2A+SSlqBSlrVVmaPOXP725uaGQmcpFFfMgc0yetNDzgJ+6ZwbPUykqJeHclrrCVT3xwTL7Udmr4y1HSCXyu1fdIjtAEr1VGs1ry1BkbYbG5pmWYiTFyI4jfRb/w93dv29mcXDn5MpU8AMtZ3raJzWevY9+ClqZ15I67q/M/ztn6d920pqpy+2Ha1YlNXBRrmVYFn94yh/aKnGdH1sYjq9ur9ISc9fmZHAnoScsvUXznmh3zjzY03UlsQN//X/+F8kkyK9fxu1sy4itXcTpqLadqxiMsh2gFzSElTKspYwga6PLalvLLmfuiKkDjFg/V+ceU0/dxaPXicTJlFiU5O171iDqagi2G8gIoI4Gdq3rSIy/2rb0YpJWY3AL7cS1NOhquQ0h82rvg+DIrsuaGufYytPT6Ql+NIambj9AWdheLlz1tQ2qksi99GIk6bxyfsZdsUn2P+rzxEeP4eWtX+lZtKZHHjos0TOvprqU0riYDbf9EiwiOmRoCo5LWEqOv/5/Lb2LaZIp/sSoX0fg9b/0Tmj7V5n8djNMmYcZTJQovHpX9J/zmUEqmsB6BdbQGjAMDKNewlPPJ22TU9oCXoqxt25vHb7XZe12A6SC+VWgnokqEpOc5hXVjcWkaWHGyZajPMaSQm9+Lw7Zed9zqL+f3HnTU1SWZZTk3VsX03HjjU0r3yY1P5tHPrjN6k6eToVg0fjdrQictRBvH41GG/1k5JXbiWoR4Kq5LRUm6ojHw903TWnZDJWVzsQoXmXDN3we9ebmuwlGT4BmGAzUyGMeOcX/v3x3nvvZNCim0i+vJGKISdz6M/fZuD8t1tMV3QG4a3gUPLKrQT1SFCVnNbqVxaBvrax2cq76zap3PS0O3XPz53FkX+6s3w/NdmId9wFQHi8d4lz1I3fshmnGJXN4JiyKcFENFYLVJ1wQ6WKTFs13qryIs3XNjUXZLJsV6jfJiPrfuO8zv2Fs3DSXgbr1GSqJ7QEi5CeClUlqa2KWoBYKr06LHJuPl5DBLeFcOIxd8bBe53FQ550p+nUZKovymaEaDmVoJ4KVSVHIJMOmWqAD9Y3DMzlvh0xBzbJmE0POQvMQ8650XoGTMvl/pWv6ZFgEepnO4BSvdAKRCpEtp/b3tGnybJFyDTQb8M/3Dn1P88sHr5CTi3aqclUyRtgO0CulFMJ6vhlVXJc45Xgxa1t2+nF7PwZCby8XsZtfdBZWPlbZ36shRq9kU0VQtl0R9l8IWgJqhLkBGhHxP1gfcOU7mwvQvIgkfWPOPOa73UWj1kv4ycCo/IcU6mugrYD5IqWoFIWZYJ0nOS4K0dlnGPegJ6W4I7VMmnHA87C8B+cM6e2UV2QEaRKHYeWYBHSElQlJxUi+a7GplTnx0Ro28vg9cudM9vvcxaN2yqjTwF8t8K8KmpagkVIS1CVHAlJ8uqm5rkdUrH1OXfKrvu9qcmmpag43XY2pY5DS7AIubYDKNVTh4de0vi13Sf9+XDypdqg2x6OsL3jKravsJ1LqeNpC9Ycgstsx8iJcipBPRJUJaWl36gXd4x548JaY6r7hc923cxLG5zkqoNu+qXRkJlsO59SxzIkXb/SdoZc0RJUygLBOCvmfLgd490ob4wJBCtOmR6s8C79uU79Lie5equT2tgfaZsBr15uSSnLHNsBckVLUCkLElOuedwJhc871ucDwUFjAjXnj6moOR+RZJOT3PC8k3zBFfdQjDKarUOVLC3BIqQlqEpCc+3JW/aOOPOs7m5vTNWAUPWcs0PVcxBxHTez/QWnY/VhN7NzLDhlv8SRKkpagkVIB8aooueaQGbl7KUpjOnViifGBILBigkzgxVe97nOoe1Ox6odTnpTBOmYTnl9T6vipSVYhPRIUBW9xJRrn3BC1cc8DdpTgeCQcYF+F4yr4ALEbW9wUus3OMm1iFs/DYjk6nWU6kJLsAhpCaqi1tR/7OZ9w0/P2/JFJhAeGKo+bX6o+jRE3IybfnGVk1zV5GZ2jwNXb7ZXudRoO0CulFMJdtgOoNSxuCaQWTV7aQZjKgvxesYEQsHKSXOClZO818/s3+okV+100luGIMlpQKAQOVTZOmQ7QK6UUwkesB1AqWPZEL3+CSdYlbPToD0VCA2bGAhdPLGCixG37ZCTXJtwUutC4jZOA/rbyqVKlpZgEaoH0uj9VKrINPYft3H/sHnzbec4wgRqhoTCZ54TCp+JiJNy05tXZJJrWiTz8kSQMbbzqZKgJVhsYnUJSURjB4GRtrModYRrAulVs28FY4ryzZkxwcpgZXResDIKgJvZszmTXLXbTb04FFJTAWM3YXHoSGcQEcKVRfnPaIOWYJHaj5agKiLrY+960rV4GrSnAqGRkytDIyfTD8RtOZBJvrDRSW2owG2aAdTYzpdPjuvy+T/8g8H9vC9zxugRPP3iS9y84Ay2H6xn+ugRlhMWlcO2A+RKOZagUkWhYcD4ugNDZ7/Odo7eMoHaoRXh+UMrwvMRyXS4qY3PZZJr2sXZNxmk7N5s7mlsZvbJo3jDrBgAD61Yy2Uzo2w/WI8jLqGgjiXqREuwSGkJqqLgmmBq9axbAxhTFt9jxoSqg1XTTg9WTQPAzexOZDpW7XfT24ZBOmY5Xk7sOFRPYs9+th44xMhIf0LBIGnHZdvBw5wfnWg7XjFpuv2Bh9O2Q+RKWXyDdqIlqIrCuqnvfsoNVpbMadCeCoRGxyprR8cAxG3ak+lYs9lJJcJIywyg2nK8Xjl58EDee94IBoSrue+Z1QysqeLxzduYMXoEv1udYPro4cw9ZbTtmMWgbK4HgpagUjlXH5m04eBJM0v2NGhPmcCAkRU1546sqDkXkXSbk6p7xkmuSYlzIAoy1Ha+7hqVPfoDGDMogiPCO8+aw45DDQxJptiy/6CWoEdLsIhpCSqrXBNKrpm5pLJcToP2lDEVNaGqGWeGqmYgIuJmdq53kqsOuOkdRb9G4r3PrOGCqZMYMaA/617ex+LYJLYeOMTkYSfxckMTOlD238rmeiBoCSqVU2un3fR0OZ8G7QljjAlWjJ0WrBgLgOs0ZNdIrKvNrpFYkNlzuuvCaZO59+lVCDBt1DAmDRvC+t176V9dxfZD9UwfNdx2xGJRVkeCRqR8ptxMRGNnAM/YzqH8qX7g5PWrZt0WxZig7SzFTiTZ7CQT653UCxlxDsaAIbYzqW775u0PPHyb7RC5Um5Hgjp1mrIiexq0Wguwe4yp6h+qnn1WqHo2IuK6me1rnY5Vh9zMzpPB0aGYxW2b7QC5VG4lqKdDlRUvTH/P026gQk+D9oIxJhCsGD8jWDEeANc5vMNJrtrupDZFkHZdI7H4bLUdIJfK6j9XrC7RmojG6oFBtrMo/zg8KLr28OCp59rOUS4CwcGnBGoWn1JRsxhxOxqd1Pr1TnKtiHt4GjDQdj6lJVjsNgC+GZ6u7HICoY41M26pxRidTiQPTKA6EqqeNz9UPe/IGomrneTqRjeza5yukWiFoKdDi956tARVgbww/X3PiJ4GLYjsGomzX1kj8cCLTnLVS056yyCkYzqg12Pzb8/tDzzcbjtELpVrCSqVd4cGxV6oHzRFT4NaEggNnRAIXTShgosQt+2wt0bi+oC4DdOAAbbzlamcngo1xtwBNIvI97N//hDQICI/7rTN/wCPicgfjTHjgc+JyDtzlUFLUKlecAIV7S/MuGWAngYtDiZQMzgUPvN12TUS0256y8pMcnWzZPZMBFfXSMydjbnakTFmOTAeSBlj3ph9eDzgGGMWi8g1xhttfTHwdWPM5cATeOvG5oyWoFK9sGbG+5+TQGiB7RzqtYwJVgQrp8wNVk4BwM3s25xJrnzZTW8dgqSmAvrGpfcSudqRiFxmjFkKDAX+mH34LcAqEbkn++e34o3zMMA7gMeBMcaYLwDjROTqvuYouxKM1SX2JqKxw8Bg21lUeTo4ZPqahoGT9TRoiQiEhk+uDF06GUDc1gOZ5JqNbmpDhbhN04F+luOVmpyVYCfVQG3246ojDxpjqoA78A5sZgKnA/fgner+JfCVXLx42ZVg1npAf0ipnHMCla1rp908EGN0IskSZAL9hlaE5w/l32skbn4+k1zdJs7eslwjMQ/yUYKXAfOyH48Bns5+fAbwIN7p0CuAVcB7gC8DqwEnFy+uJahUD6yeueR5CYR0NGgZ8NZIjJ0WrPKWQ3QzL9dlOlbuddPbhkM6is6Y3VUbsCMXO8pe63Ozf7zryOnP7OlRjHet/Qm80o0BnwS+nd0+BPwQWJGLLOVcgkrl1IEhM1c3RibqdcAyFQiNilbWjooCiNu8N5Ncs9lJbqjKrpEYthyvGGy8/YGHczXZ9HXAVXj3HRpjzFXZx032sauB7wJPdXleKLvNh4CQMWaIiPRpQm8tQaW6IROsalk37aYhehrUH0yg/4iK8DkjKsLnHFkj8Vkn+UJSnP1TQIbZzmfJ6lztKHsLxI+NMR8BThKRjwIYY34FPCIiy7J/Pin7FAeI4E2NeYOIpI0xt+GN/fhUX7JoCSrVDatnfmClBIJ6FOhD2TUSz3hljcRdG5zkqv1uevtIyEyxna+AnszVjowx1cB9eD+rP97pU+8AvmyMuUdEbsBbbqtaROqNMZuAP3tPNwANwPv7nKWcllLqLBGNHQBOOuGGSp3A/pNmr1w37aY5ehSounKdxt3ZNRJrkNaZFNkaiTk2/fYHHs7ZAYYxZpyIbD/G54aLyL5cvdbxlOuRIHjvMHQAg+qTTLCqef3Udw3TAlRHEwhGRgdqzhtdUXMeIqkWJ5VY6STXOOIcjFJeayQ24t2vlzPHKsDs5wpSgFDeJbgCLUHVR6tm3bpaAkEdaaxOyJjK2lDVrLNCVbOyayTuWOskVx1y0y+VwxqJz+RwUExRKecS/AfwYdshVOnaN3TuiuYB47QAVY95aySOmxGsGAeA6xx+yUmu3uakNvZH2mcAFVYD9lzXUZplo5xL8DG8EUU6s7zqsUywumn91Bv05mmVE4Hg4LGBmkVjK2oWleoaiWVbgmU7MAYgEY09B5xmO4cqPc/N++hjzf3H6lGgyitvjcRt65zkqkY3s2ssuONtZzoKAQbd/sDDjbaD5EM5HwmCd0pUS1D1yN5hpz2vBagKwVsjceLsYKV3ydB1Dm5zOla95KQ3DyyiNRI3lGsBgj9K8CO2Q6jSkQ6FGzfErh9tO4fyp0DwpPGBfheOr+BCxG2vd1JrE05ynbG8RmLZngqF8i/Bx4AM5f91qhxZNXvpOkzgdbZzKGUC4UGh6jPmh6rPyK6RuHVlJrm6RTIvjwf35AJG0RIsVbG6REsiGlsBnGk7iyp+e0ac9WxL7RgtQFV0vDUST50brDwVADezb0smuWq3m94yGElNI79rJGoJlrh/oCWoTiAdqmlITHnnWNs5lOqOQGj4pMrQJZMAxG096CRfqHNSG0LiNk7nlbX5cuEQUJfD/RUdv5TgnbZDqOK2cs6H1utpUFWKTKDfSaHw2eeEwmcjkkl6aySuaRNnzySQUX3c/SPlepP8EX4owSeANKV3c6oqkJdHnP1sa79RWoCq5BkTquqyRuKmTMeql930i8MgHaPnayT+Iechi0zZl2CsLtGaiMaeBfSHnHqNdKhffd2Ud4yznUOpfAiERp1aWTvqVABxm/dlkms2OclEFdLcnTUSXeBPeQ9pWdmXYNY/0BJUR7FizocTmMB82zmUyjcT6D+8InzO8Owaie1OauNzTnJNhzj7TwUZfpSnPHf7Aw8fLHjQAvNTCf6X7RCquOweec7Tbf1GaAEq3zGmIhyqmn56qGo6IiKS2b0hk1x5wE1vH9FpjcSyPxUK/inBx/GWAonYDqKKQ6qi9tDGU68u9Zn9leozY4wxFWOmVlaMAcB1Gl92kmu2uJmdv7ccrSDyeW9J0YjVJVKAL/5BVfesmPPhTZjAUNs5lCo2gWBkVEXNgglVA9652naWQvBFCWY9ZDuAKg67Ri94qr1m+Nm2cyhVxH63ZNmisr414gg/leCfgBbbIZRdqYr+BzdNestk2zmUKnK/sR2gUHxTgrG6RAew3HYOZdeKubdvxgROsp1DqSLWADxqO0Sh+KYEs35pO4Cy56Uxi55qDw/V06BKHd/DS5YtStsOUSh+K8E/AG22Q6jCS1YOOLBl4pVTTrylUr73f7YDFJKvSjBWl2gDfmc7hyq8FXPueBFjBtvOoVSRexn4q+0QheSrEszy1bscBTtOvuDJjvAQXUlEqRO7d8myRY7tEIXkxxL8M3DAdghVGMnKyP6tEy6P2c6hVIn4ie0Ahea7EozVJTLAg7ZzqMJ4fu4d2zFmkO0cSpWA1UuWLVpnO0Sh+a4Es/SUqA9sH3vxE8nqwWfYzqFUifip7QA2+LIEY3WJp4GttnOo/OmoGrj3xfFvnG47h1IlwgHutR3CBl+WYJbvzn37yYo5d+zEGJ0wXanueWTJskX7bIewwc8l+H0gaTuEyr1tp1z6eLJ60Om2cyhVQnx5KhR8XIKxusR+4H7bOVRutVcN3rNt3GUzbOdQqoTsB35tO4Qtvi3BrK/bDqBya8Xc23fraVCleuT7S5Yt8u1ZMV+XYKwusRr4p+0cKjdeHPeGx1NVA0+znUOpEpIGvmc7hE2+LsGsb9gOoPquvXrwy9tPuWSm7RxKlZgHlyxbtMd2CJu0BOG3wDbbIVTfrJj7kT0YM8B2DqVKjO8vCfm+BGN1CRf4lu0cqve2jn/TY6nKAfNs51CqxDy5ZNmi522HsM33JZj1I6DZdgjVc23hk3btGHvRHNs5lCpBeikILUEAYnWJJuAe2zlUzwjIijl3HMCYWttZlCoxO4Ff2Q5RDLQEX/FNwLUdQnXflglXPJau7K9HgUr13HeWLFuUsR2iGGgJZsXqElvwVp5XJaAtPHTnzpMX63VApXquGfiB7RDFQkvw1Xw/UqoUCMiKuXccwph+trMoVYK+sWTZosO2QxQLLcFOYnWJvwErbedQx7d50n88lq6onW07h1IlqBH4qu0QxURL8LU+bjuAOrbW8PAdu0afr7PCKNU7X1+ybFG97RDFREuwi1hd4s/A323nUK/lnQa9vQFjamxnUaoENQBfsx2i2GgJHt1HAbEdQr3apslvfSxT0W+W7RxKlaivLlm2qNF2iGKjJXgUsbrE88AvbedQr2itGbF996gFukagUr1zGL05/qi0BI/tE4DeR1MEBOOumHN7M8aEbWdRqkR9ZcmyRU22QxQjLcFjiNUlNgM/tJ1DwcZTr34sU1GjC+Uq1TuH8CYDTGZJlgAAEEZJREFUUUehJXh8nwZabYfws5Z+o7a9PPKcM23nUKqE3bVk2aIW2yGKlZbgccTqEnvRG+itEYyzYs6H2zCm2nYWpUrUZvQo8Li0BE/si8BB2yH8qG7KOx5zQuFptnMoVcI+tGTZopTtEMVMS/AEsitM/I/tHH7T3G/01j0jzj7bdg6lStgflyxbtNx2iGKnJdg93wV22A7hF4JxVs75cBJjqmxnUapEpYEP2Q5RCrQEuyFWl0gC/207h18kotc+5oSqp9rOoVQJ+9aSZYs22g5RCrQEu+//gH/aDlHummpP3rx3+BnzbedQqoTtBz5jO0Sp0BLsplhdQoCbgQ7bWcqVawKZVbOXZjCm0nYWpUrYx3V6tO7TEuyB7A30cds5ylUiet3jTqg6ZjuHUiVsBfBj2yFKiZZgz30Z7z+ayqGm/qds2jfsND0NqlTvOcD7lixb5NoOUkq0BHsoVpdwgHej84rmjGsC6ZWzb3P1NKhSffKVJcsWPWc7RKnREuyFWF1iDfAl2znKxYbYDU+4waqo7RxKlbA64JO2Q5QiLcHe+zSgQ5D7qHHA+I37h859ne0cftPa0URi1/O0tJ94/ERT22EcR098FDEXuHHJskVJ20FKkZZgL2XvHbwJXXy311wTSK+adavBmArbWfykLdnMsj99gh37N/KNh2+nub2Bnz/6Jb786w/wp5X/B8A/1/2GL/7q/STT7SR2PU8wGLKcWh3H15YsW/S07RClSkuwD2J1iceB79nOUarWTX33k26w8lTbOfxm96EXufLs93HJ3HcSG3Mam3avwhWXO674Ngeb9rC/cRe7Dm1hfvRSdhzYSGVI5y8vYpvQiTz6RN/e9d2dwBuBk20HKSUNkYmJgyfN0tOgFkweNQuALS+/wI79dbQlm5k7cSEA0THz2LpnHYjguA51O5/nkrnXWEyrjsMF3rVk2aJ220FKmR4J9lGsLtEM3GI7RylxTTC1euYHQhijb8IsERFWbP0HNVX9MUCk30kA9KsaQHN7PdExp7Fux9MMrB3K9//832zavcpuYHU031qybNETtkOUOi3BHIjVJf4A/Mx2jlKxdtpNT7nBysm2c/iZMYarz72N0UMm8OK+DaQz3piKZLodEZd5k87n9addR7iylmljz2T1tscsJ1ZdbAY+bjtEOdASzJ334w1TVsdRH5m84dCQGefYzuFnf1l9H89segSAtmQLF85+G1v3rgNg96GtDO4/AoADjbsZ+v/bu/tgqcrDjuPfcy+IaEBArQjxNaL3iIqSCWJUtCfapjUJNtVGpzFVienW0ybi2KTJTK3RpM500pjGrnPGZKYdUieaRhNbSTK13UFb8A0ERN0VNOMLalSKiry/uP3jbMOVgHBhd5/dPd/PzJm97L1398cMsz/Oc57zPKMnMKx/OPW68786yCbgj9IsWR86SC+wBJskrlXXAhcB60Jn6VTvRsM2LT0lHUEU9YfOUmRnxp/g0eX3c8u911Cvv8uUo8/kseX3c/eC23j8lw9w0pGns2HzOkYdMJbxY49ifnUuJ0ycGjq2tpudZsmS0CF6ReT/8JqrOhB/FodGd2rJyVc/sPrgyeeEzqHftH7TO9RWLuK4w09h9AHjQsfRrt2VZskloUP0Es8EmyyuVf8FyELn6DSrxxz/1OpxJzoM2qEOGDGKqR861wLsbM+S72SjJrIEW+MaYGHoEJ1iW9+wjUtPuXqkw6DSXtsEXJxmyTuhg/QaS7AFGqvJXAysDp2lEyw7qfRIvW/4saFzSF3sWq8DtoYl2CJxrfo88DkKvqza/46Nl60eO3B26BxSF/tRmiW3hQ7RqyzBFopr1bnAzaFzhLKtb/iGJ04ufYAo8t+ZtHdW4HXAlvLDqfWuByqhQ4TwxMl/9mi9b9gxoXNIXeot4JNplqwJHaSXWYIt1tiE91LgldBZ2mnVuMlL3xxzvMOg0t7ZClyUZonbtbWYJdgGca36OvAZYEvoLO2wrW+/9ctO+sIYh0GlvZamWfJfoUMUgR9SbdLYdmkWBZgos/SUqxfW+4YdFTqH1KVuSbPk9tAhisISbKO4Vv0B8OXQOVrpjYNPXvLWQcc5DCrtnfuA60KHKBJLsM3iWvVbwN+HztEK2/r2W/fk5KsOJoqi0FmkLrQUuDTNkndDBykSSzCMv6QH1xddMuXPF9X7+t1cWBq6X5HPBF0bOkjRWIIBxLVqHbgS+EXoLM3y+iFTFr89+liHQaWhWwd8Ks2Sl0IHKSJLMJC4Vt1KvvXSo6Gz7Kut/SPWPnXirEMdBpWGbDNwYZolj4UOUlSWYEBxrboOuADo6nuBlkz5i8X1vv4Phs4hdZlt5NcA/zN0kCKzBAOLa9VVwO/SpTfTv3bo1EVrRh/jMKg0NHXgqjRL7gkdpOgswQ4Q16ovAB8nXyapa2zt33/N0/Hl40PnkLrQ7DRL/il0CFmCHSOuVZcBM4GNobPsqcVTvri03tc/MXQOqct8Jc2SfwgdQjlLsIPEteqD5JNlOr4IX/utDy98Z/RRDoNKQ3NDmiV/FzqEtrMEO0xj+6XfAzp2B+kt/SPffir+E88ApaG5Oc2Sr4cOofeyBDtQXKvOA86jQ3emX3zql5YR9R8eOofURf4mzZKvhQ6h32QJdqi4Vn0UmAG8GjrLYK8eNu2xtaOOOCt0DqlL1IEvpVlyY+gg2jlLsIPFtepTwNnA84GjALBl2Mi3qwOXuSyatGe2AZenWfLd0EG0a5Zgh4tr1eeAs4Bq6CyPnzp7GVGft0RIu7eJfFPcOaGD6P1Zgl0grlVfJh8aXRQqwyvjpz+67gMTHQaVdm8tcEGaJT8NHUS7Zwl2icbKMgnwYLvfe8uwA9+snfDHbpIr7d6bwHnuCt89LMEuEteqa8hXlvlZO9930WmznybqO6yd7yl1oVeBc9IseSR0EO05S7DLxLXqBuBC4K52vN/Lh5/5yPoDDz+zHe8ldbHFwOlpliwLHURDYwl2obhW3QJcCtxEPgW7JTYPP3D1M8dfcmyrXl/qEfcAZ7kfYHeK6vWWfYaqDaoD8UxgDjC62a/98LS/XrD+gPEfbfbrSj3kJvIb4f0g7VKWYA+oDsQDwE+BE5r1misnzHh4+fGfmd6s15N6zEbgijRL7gwdRPvG4dAeENeqNWAacG8zXm/z8FGrlk+6+LhmvJbUg14FZliAvcES7BGNmaN/AFzPPl4nXHTatSuI+g5pSjCptywCPpJmyWOhg6g5HA7tQdWB+ALgDuCgof7uSxPPfWjFpIvPaH4qqevNAUpplmwIHUTNYwn2qOpAPAn4CTB5T39n0/BRb8z/6N/2EfUd3LpkUtdZC6QugdabHA7tUXGtugKYDty9p7+zaOp1z1mA0nssAT5sAfYuS7CHxbXq2rhWvQj4KvmK9rv04gc/tmDjyEOcDSpt94/A9DRLlocOotZxOLQgqgPx6cAPgEk7fm/Tfge9Pv+Mbw4nisa2P5nUcd4EZqVZ8pPQQdR6ngkWRFyrPgKcCty24/cWTr3ueQtQAuAh4DQLsDgswQKJa9X1ca2aki/C/QrAC0ecP3/T/uOmhU0mBbcFuJH8/r8XQodR+zgcWlDVgXjsxv3GfGvBGd/4NFE0JnQeKaCFwJUufl1MngkWVFyrvnnaEw/NIopmAa+FziMFsAH4MvnkFwuwoDwTFOVSZRzwHeCy0FmkNnkQ+HyaJStCB1FYlqB+rVyqXEA+cebI0FmkFnkH+AqQufODwBLUDsqlykjgr8iHifYPHEdqpp8Df+q+fxrMEtROlUuVo4Fvky/KLXWzFcB1aZb8W+gg6jyWoN5XuVQ5H/guMBA6izREb5FventrmiVbQodRZ7IEtVvlUmU48EXybZqavoO91GTbgNuB69MsWRU6jDqbJag9Vi5VxgM3A5/D22vUme4Hrk2z5MnQQdQdLEENWblUmQx8Hfg0EAWOIwEsJ7/u9++hg6i7WILaa+VS5VTypaY+GTqLCmsF8A3gjjRL3nenFGlnLEHts3KpMo18AsLvhM6iwniGvPx+aPlpX1iCappyqXIW+QfTOaGzqGfVyP/DdWeaJe+GDqPuZwmq6cqlysfIb7b3zFDN8jR5+f3I8lMzWYJqmcYEmmuAz+LqM9o7C4BbgLtd5kytYAmq5cqlyqFACbgaGB84jjrfRuBO8pvcHw8dRr3NElTblEuV/YBLgNnku9xLg71EvoD7973JXe1iCSqIcqlyLvmZ4aeAEWHTKLB5wK3Avc70VLtZggqqXKqMJT87vByYFjaN2mgV8EPge25oq5AsQXWMcqkyQF6GlwETwqZRC2wG7gPmAD9zUWt1AktQHadcqvQD55EX4oU4s7SbvQs8QD7R5cdplqwOnEd6D0tQHa1cqowGPg7MBH4fGBM2kfZAHXgEuIv8vr5XAueRdskSVNdobOk0g7wQZwJHhk2kQd4G/gOYC/w8zZLXA+eR9oglqK7VWMB7JvkM06mB4xRRjfwa31xgvtf41I0sQfWEcqlyOPlZ4jmN48SwiXrSW+QruPwCmJtmyS8D55H2mSWontRYpeb/S3EGcArufThUzwHzBx1Pu3SZeo0lqEJo3I94NnAGMKVxeBvGdluAxcD/kBfegjRLfhU2ktR6lqAKq1yqHEJehqeyvRhjYHjIXC22jfwM70ngqcbxJLDca3oqIktQGqSxvmkMTAaOBT406HEC3TGkWgdeA1aSr8f5DNtLr5ZmycaA2aSOYglKe6hRkBPJb804onFMAMYOOsYM+rqZa6JuBd4ZdKwBXmV70a0cdLzsWZ20ZyxBqUXKpcpItpfiaKAf6Gsc0S6+BlhHXnK/Lr00Sza0NbxUEJagJKmw+nb/I5Ik9SZLUFJwURTdEEXRua36eWlXLEFJUmENCx1AkhrOj6LoRvJJRJ8Avg8cCDxbr9eviKJoLPCv5BOMImBeFEUjgH8mn6W7Erii8b0dn/sacDpwAPAGcEm9Xt/atr+ZOpZngpI6xXH1en0GcA8wC7iVfF/Jo6MoOgz4AnBfvV7/bfIVbgCuAp6s1+vnACuAK3fxHMB/N557jXzhdckSlNQx5jQeXwReAD4P3AGMA0YCxwBLGz+zsPF4IvnehQAPky90sLPnABY1Hp8Ajm56enUlS1BSp1g36OubgB8Dlw56/kXylXwgX+oO8lVwpje+ns72peB2fA5gWuPxNODZZgZX97IEJXWi7wFfBSqNP08Ebgf+MIqieeTXDSG/bjg5iqIHgUnk1wJ39hzARxq/O4Z8H0TJm+Ul9b4oim4A5tXr9XmBo6jDWIKSpMJyOFSSVFiWoCSpsCxBSVJhWYKSpMKyBCVJhWUJSpIKyxKUJBWWJShJKixLUJJUWJagJKmwLEFJUmFZgpKkwrIEJUmFZQlKkgrLEpQkFZYlKEkqLEtQklRYlqAkqbAsQUlSYVmCkqTCsgQlSYVlCUqSCssSlCQVliUoSSosS1CSVFiWoCSpsCxBSVJhWYKSpMKyBCVJhWUJSpIKyxKUJBWWJShJKixLUJJUWJagJKmwLEFJUmFZgpKkwrIEJUmFZQlKkgrLEpQkFdb/AWXAmbsnAiaeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].values.sum()\n",
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['bd_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的大数据工具')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习工具 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_266a34a8_8b6b_11ea_b748_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >ml_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow0_col1\" class=\"data row0 col1\" >mxnet</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow0_col2\" class=\"data row0 col2\" >26800</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow0_col3\" class=\"data row0 col3\" >22500</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow0_col4\" class=\"data row0 col4\" >12017</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow0_col5\" class=\"data row0 col5\" >62500</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow0_col6\" class=\"data row0 col6\" >91</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow0_col7\" class=\"data row0 col7\" >10.33%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow1_col1\" class=\"data row1 col1\" >pytorch</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow1_col2\" class=\"data row1 col2\" >26022</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow1_col3\" class=\"data row1 col3\" >20833</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow1_col4\" class=\"data row1 col4\" >12094</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow1_col5\" class=\"data row1 col5\" >59437</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow1_col6\" class=\"data row1 col6\" >135</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow1_col7\" class=\"data row1 col7\" >15.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow2_col1\" class=\"data row2 col1\" >keras</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow2_col2\" class=\"data row2 col2\" >25447</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow2_col4\" class=\"data row2 col4\" >8975</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow2_col5\" class=\"data row2 col5\" >62500</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow2_col6\" class=\"data row2 col6\" >79</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow2_col7\" class=\"data row2 col7\" >8.97%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow3_col1\" class=\"data row3 col1\" >tensorflow</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow3_col2\" class=\"data row3 col2\" >24492</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow3_col3\" class=\"data row3 col3\" >22500</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow3_col4\" class=\"data row3 col4\" >11625</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow3_col5\" class=\"data row3 col5\" >53864</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow3_col6\" class=\"data row3 col6\" >310</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow3_col7\" class=\"data row3 col7\" >35.19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow4_col1\" class=\"data row4 col1\" >caffe</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow4_col2\" class=\"data row4 col2\" >24096</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow4_col3\" class=\"data row4 col3\" >20000</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow4_col4\" class=\"data row4 col4\" >12500</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow4_col5\" class=\"data row4 col5\" >61000</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow4_col6\" class=\"data row4 col6\" >172</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow4_col7\" class=\"data row4 col7\" >19.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow5_col0\" class=\"data row5 col0\" >8</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow5_col1\" class=\"data row5 col1\" >sklearn</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow5_col2\" class=\"data row5 col2\" >20617</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow5_col3\" class=\"data row5 col3\" >17500</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow5_col4\" class=\"data row5 col4\" >9467</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow5_col5\" class=\"data row5 col5\" >45000</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow5_col6\" class=\"data row5 col6\" >64</td>\n",
       "                        <td id=\"T_266a34a8_8b6b_11ea_b748_701ce71031efrow5_col7\" class=\"data row5 col7\" >7.26%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25eb599a588>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'ml_')\n",
    "data_pl=data_pl[data_pl['head_count']>50]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9659477866061293"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl['percentage'].values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].values.sum()\n",
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['ml_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的机器学习库')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 英语"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_=data.shape[0]\n",
    "en=data[data['english']==1].shape[0]\n",
    "other=all_-en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie([en,other], labels=['要求英语','没说'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_counts=data[~(data.edu=='')].groupby(by=['edu']).headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu\n",
       "博士     20\n",
       "大专     65\n",
       "本科    506\n",
       "硕士    241\n",
       "高中      5\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(edu_counts.values, labels=edu_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >edu</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow0_col1\" class=\"data row0 col1\" >28125</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow0_col2\" class=\"data row0 col2\" >17500</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow0_col4\" class=\"data row0 col4\" >40000</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow0_col5\" class=\"data row0 col5\" >20</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow0_col6\" class=\"data row0 col6\" >2.39%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow1_col1\" class=\"data row1 col1\" >25228</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow1_col2\" class=\"data row1 col2\" >7564</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow1_col4\" class=\"data row1 col4\" >59708</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow1_col5\" class=\"data row1 col5\" >241</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow1_col6\" class=\"data row1 col6\" >28.79%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow2_col1\" class=\"data row2 col1\" >21149</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow2_col2\" class=\"data row2 col2\" >7860</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow2_col4\" class=\"data row2 col4\" >45000</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow2_col5\" class=\"data row2 col5\" >506</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow2_col6\" class=\"data row2 col6\" >60.45%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow3_col1\" class=\"data row3 col1\" >11862</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow3_col2\" class=\"data row3 col2\" >7000</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow3_col3\" class=\"data row3 col3\" >11500</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow3_col4\" class=\"data row3 col4\" >30525</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow3_col5\" class=\"data row3 col5\" >65</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow3_col6\" class=\"data row3 col6\" >7.77%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow4_col0\" class=\"data row4 col0\" >高中</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow4_col1\" class=\"data row4 col1\" >7600</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow4_col2\" class=\"data row4 col2\" >4750</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow4_col3\" class=\"data row4 col3\" >7083</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow4_col4\" class=\"data row4 col4\" >12000</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow4_col5\" class=\"data row4 col5\" >5</td>\n",
       "                        <td id=\"T_2ea8a12c_8b6b_11ea_964b_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.60%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25eb5904088>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['初中及以下','高中','大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是12396，20551，24723，28571。中位数分别是11500， 17500，22000， 27500。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=0.7107033855339437, pvalue=0.401755658573565)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=8.790505903778886, pvalue=0.003319569067495765)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=16.903656058208323, pvalue=5.728614488062322e-05)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >experience</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow0_col0\" class=\"data row0 col0\" >10+</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow0_col1\" class=\"data row0 col1\" >28250</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow0_col2\" class=\"data row0 col2\" >26500</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow0_col3\" class=\"data row0 col3\" >28250</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow0_col4\" class=\"data row0 col4\" >30000</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow0_col5\" class=\"data row0 col5\" >2</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.23%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow1_col1\" class=\"data row1 col1\" >25714</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow1_col2\" class=\"data row1 col2\" >7225</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow1_col3\" class=\"data row1 col3\" >25000</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow1_col4\" class=\"data row1 col4\" >60000</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow1_col5\" class=\"data row1 col5\" >49</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow1_col6\" class=\"data row1 col6\" >5.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow2_col1\" class=\"data row2 col1\" >25257</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow2_col2\" class=\"data row2 col2\" >10060</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow2_col4\" class=\"data row2 col4\" >46067</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow2_col5\" class=\"data row2 col5\" >284</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow2_col6\" class=\"data row2 col6\" >32.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow3_col0\" class=\"data row3 col0\" ></td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow3_col1\" class=\"data row3 col1\" >20087</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow3_col2\" class=\"data row3 col2\" >7000</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow3_col3\" class=\"data row3 col3\" >17500</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow3_col4\" class=\"data row3 col4\" >56047</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow3_col5\" class=\"data row3 col5\" >189</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow3_col6\" class=\"data row3 col6\" >21.58%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow4_col1\" class=\"data row4 col1\" >19414</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow4_col4\" class=\"data row4 col4\" >40140</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow4_col5\" class=\"data row4 col5\" >352</td>\n",
       "                        <td id=\"T_36585d68_8b6b_11ea_ba8d_701ce71031efrow4_col6\" class=\"data row4 col6\" >40.18%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25eb569aa48>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'company_type'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'company_type'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-55-8570df4a4801>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata_company_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_sub_stats_by_col\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'company_type'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mapply_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_company_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-15-d1d14471b9f6>\u001b[0m in \u001b[0;36mget_sub_stats_by_col\u001b[1;34m(data, col)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Common Functions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_sub_stats_by_col\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mcategories\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0msalary_mean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0msalary_95_min\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2798\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2799\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2800\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'company_type'"
     ]
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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